Base_JSON/prefixO/json/O13/O13-2004.json

{
    "paper_id": "O13-2004",
    "header": {
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        "date_generated": "2023-01-19T08:03:50.388074Z"
    },
    "title": "Emotion Co-referencing -Emotional Expression, Holder, and Topic",
    "authors": [
        {
            "first": "Dipankar",
            "middle": [],
            "last": "Das",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "National Institute of Technology (NIT)",
                "location": {
                    "postCode": "793003",
                    "settlement": "Meghalaya",
                    "region": "Shillong",
                    "country": "India"
                }
            },
            "email": ""
        },
        {
            "first": "Sivaji",
            "middle": [],
            "last": "Bandyopadhyay",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Jadavpur University",
                "location": {
                    "addrLine": "West Bengal",
                    "postCode": "700032",
                    "settlement": "Kolkata",
                    "country": "India"
                }
            },
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "The present approach aims to identify the emotional expression, holder, topic, and their co-reference from Bengali blog sentences. Two techniques are employed, one is a rule-based baseline system and the other is a supervised system that consists of different syntactic, semantic, rhetorical, and overlapping features. Different error cases have been resolved using rule-based post processing techniques. The evaluative vectors containing emotional expressions, holders, and topics are prepared from annotated blog posts as well as from system generated output. The evaluation metric, Krippendorff's \u03b1, achieves agreement scores of 0.53 and 0.67 for the baseline and supervised co-reference classification systems, respectively.",
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        "abstract": [
            {
                "text": "The present approach aims to identify the emotional expression, holder, topic, and their co-reference from Bengali blog sentences. Two techniques are employed, one is a rule-based baseline system and the other is a supervised system that consists of different syntactic, semantic, rhetorical, and overlapping features. Different error cases have been resolved using rule-based post processing techniques. The evaluative vectors containing emotional expressions, holders, and topics are prepared from annotated blog posts as well as from system generated output. The evaluation metric, Krippendorff's \u03b1, achieves agreement scores of 0.53 and 0.67 for the baseline and supervised co-reference classification systems, respectively.",
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                "section": "Abstract",
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                "text": "In psychology and in common use, emotion is an aspect of a person's mental state of being, normally based on or tied to the person's internal (physical) and external (social) sensory feeling (Zhang et al., 2008) . Emotions, of course, are not linguistic features. Nevertheless, the most convenient access we have to them is through language (Strapparava & Valitutti, 2004) . The identification of emotion expressed in the text with respect to the reader or writer is a challenging task (Yang et al., 2009) . A wide range of Natural Language Processing (NLP) tasks, from tracking users' emotion about products/events/politics as expressed in online forums or news to customer relationship management, use emotional information.",
                "cite_spans": [
                    {
                        "start": 191,
                        "end": 211,
                        "text": "(Zhang et al., 2008)",
                        "ref_id": "BIBREF40"
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                    {
                        "start": 341,
                        "end": 372,
                        "text": "(Strapparava & Valitutti, 2004)",
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                        "start": 486,
                        "end": 505,
                        "text": "(Yang et al., 2009)",
                        "ref_id": "BIBREF38"
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                ],
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                "section": "Introduction",
                "sec_num": "1."
            },
            {
                "text": "Currently, emails, blogs, chat rooms, online forums, and even Twitter are being considered as effective communication substrates to analyze the reaction of emotional catalysts. A blog is a communicative and informative repository of text-based emotional content in the Web 2.0 (Yang et al., 2007) . In particular, blog posts contain instant views, updated views, or influenced views regarding single or multiple topics. Many blogs act as online diaries of the bloggers for reporting the blogger's daily activities and surroundings. Sometimes, the blog posts are annotated by other bloggers. In addition, a large collection of blog data is suitable for any machine learning framework.",
                "cite_spans": [
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                        "start": 277,
                        "end": 296,
                        "text": "(Yang et al., 2007)",
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                "section": "Introduction",
                "sec_num": "1."
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                "text": "It has been observed that three major components are crucial in determining the emotional slants from different perspectives: Emotional Expression, Holder, and Topic. Thus, the determination of the emotion holder and topics from the text helps us track and distinguish users' emotions separately on the same or different topics. Emotional expression (word or phrase) is the subjective counterpart that can be expressed by a directly affective word (\"John is really happy enough\") or using some indirect notion (\"Dream of music is in their eyes and hearts\"). The source or holder of an emotional expression is the speaker, the writer, or the experiencer (Wiebe et al., 2005) . Extraction of the emotion holder is important in discriminating between emotions that are viewed from different perspectives (Seki, 2007) . By grouping opinion holders of different stances on diverse social and political issues, we can gain better understanding of the relationships among countries or among organizations (Kim & Hovy, 2006) . Topic, however, is the real world object, event, or abstract entity that is the primary subject of the emotion or opinion intended by the holder (Stoyanov & Cardie, 2008a) . Topic depends on the context in which its associated emotional expression occurs (Stoyanov & Cardie, 2008b) . For example, the following Bengali sentence shows the emotional expression, its associated holder, and topic. Rashed said that he was remembering this beautiful comic while reading your poem.",
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                        "text": "(Wiebe et al., 2005)",
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                "section": "Introduction",
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            {
                "text": "Emotional Expression: \u09b8\u09c1 n\u09b0 \u09c7\u0995\u09d7\u09a4\u09c1 \u0995 (beautiful comic), Holder: < writer, \u09b0\u09be\u09c7\u09b6\u09a6 (Rashed) >, Topic: \u0995\u09bf\u09ac\u09a4\u09be (poem).",
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                "section": "Introduction",
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                "text": "In the above example, along with the emotional expression and topic, the writer of the blog post is also considered as a default holder according to our assumption, which is based on the nested source hypothesis (Wiebe et al., 2005) . Sometimes, the emotional sentences may or may not contain a direct clue for the emotional expression. There are certain example sentences that contain an emotional expression without a holder (Tar Abhinoy ta satyoi khub",
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                        "text": "(Wiebe et al., 2005)",
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                "section": "Introduction",
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            {
                "text": "Emotion Holder, and Topic 81 akorshoniyo chilo [His acting was really attractive]). Nevertheless, the sentence contains a topic (Tar Abhinoy [His acting]). Sometimes, even the emotional expressions represent the potential topics. For example, the Bengali sentence, \"Ami Ramer doohkho koshte kende pheli.\" [I fall into cry on the sorrow of Ram] contains the text \"doohkho koshte\" [sorrow] that is treated as both the emotional expression and the topic. With the above examples and problems in mind, we hypothesize that the notion of user-topic co-references will facilitate both the manual and automatic identification of emotional views. Presently, we have assumed that the holder and topic are emotion co-referent if they share the same emotional expression.",
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                        "text": "Holder,",
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                        "text": "and Topic 81",
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                "section": "Introduction",
                "sec_num": "1."
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            {
                "text": "The present task deals with the identification of users' emotions on different topics from an annotated Bengali blog corpus (Das & Bandyopadhyay, 2010a) . Each sentence of the corpus is annotated with the emotional components, such as emotional expression (word/phrase), intensity (high, general, and low), associated holder, topic(s), and sentential tag of Ekman's six emotion classes (anger, disgust, fear, happy, sad, and surprise) .",
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                        "text": "(Das & Bandyopadhyay, 2010a)",
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                        "start": 386,
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                        "text": "(anger, disgust, fear, happy, sad, and surprise)",
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                "section": "Introduction",
                "sec_num": "1."
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                "text": "In this project, a simple rule-based baseline system is developed for identifying the emotional expressions, holders, and topics. The expressions are identified from shallow parsed sentences using Bengali WordNet Affect lists (Das & Bandyopadhyay, 2010b) . A simple part-of-speech (POS) tag-based pattern matching technique is employed to identify the emotion holders and topics with respect to the emotional expressions. The presence of emotion holders and topics in the immediate neighborhood, shallow chunks that refer to their corresponding emotional expressions, gives the co-reference clues for the baseline system. The co-reference among the emotional expressions, holders, and topics is measured using Krippendorff's (2004) \u03b1 metric. The error analysis suggests that the rich morphology and free phrase order nature of Bengali restricts the baseline system in capturing the holder and topic as well as disambiguating them in complex, compound, and passive sentences.",
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                        "start": 226,
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                        "text": "(Das & Bandyopadhyay, 2010b)",
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                        "text": "Krippendorff's (2004)",
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                "text": "Thus, a Support Vector Machine (SVM) (Joachims, 1998) based supervised classifier is employed as well for co-reference identification. In this classifier, each of the input vectors containing emotional expression, associated holder, and topic is prepared from each of the annotated Bengali blog sentences. The feature vector is prepared based on the information present in the sentences containing lexical, syntactic, semantic, rhetorical, and overlapping features (word, part-of speech (POS), and Named Entity (NE)). Considering each of the input vectors as a unit to be coded in terms of the values of a variable, the standard Krippendorff's (2004) \u03b1 metric produces a satisfactory score that outperforms the baseline system on the test set. This observation suggests that the adoption of error handling features, along with the features for syntax, semantics, and rhetorical structure, improves the performance of the co-reference identification reasonably. Different types of error cases have been analyzed, and we employed different rule-based post-processing techniques to solve the error cases. The rest of this paper is organized as follows. Section 2 describes the related work. The baseline system is described in Section 3. The supervised framework with feature analysis is discussed in Section 4. Experiments and associated results are specified in Section 5. The error analysis and post processing techniques are discussed in Section 6. Finally, Section 7 concludes the paper.",
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                        "text": "(Joachims, 1998)",
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                        "text": "Krippendorff's (2004)",
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                "section": "Introduction",
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                "text": "The current trend in the emotion analysis area is exploring machine learning techniques (Sebastiani, 2002; Mishne & Rijke, 2006 ) that consider the problem as text categorization or analogous to topic classification, which underscores the difference between machine learning methods and human-produced baseline models (Alm et al., 2005) . The affective text shared task on news headlines for emotion and valence level identification at SemEval 2007 has drawn focus to this field (Strapparava & Mihalcea, 2007) . In order to estimate affects in text, the model proposed by Neviarouskaya et al. (2007) processes symbolic cues and employs natural language processing techniques.",
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                        "text": "(Sebastiani, 2002;",
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                        "text": "Mishne & Rijke, 2006",
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                        "text": "Neviarouskaya et al. (2007)",
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                "section": "Related Work",
                "sec_num": "2."
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                "text": "Prior work in identification of opinion holders has sometimes identified only a single opinion per sentence (Bethard et al., 2004) and sometimes several opinions (Choi, 2005) . Identification of opinion holders for Question Answering with a supporting annotation task was attempted in Wiebe et al. (2005) . Before that, another work on labeling the arguments of the verbs with their semantic roles using a novel frame matching technique was carried out in Swier and Stevenson (2004) . Based on the traditional perspectives, another work discussed in Hu et al. (2006) uses an emotion knowledge base for extracting the emotion holder. The machine learning based classification task for \"not holder,\" \"weak holder,\" \"medium holder,\" or \"strong holder\" is described in Evans (2007) . Kim and Hovy (2006) identified the opinion holder with the topic from media text using semantic role labeling. An anaphor resolution based opinion holder identification method exploiting lexical and syntactic information from online news documents was attempted by Kim et al. (2007) . The syntactic models of identifying the emotion holder for English emotional verbs were developed in Das and Bandyopadhyay (2010d) .",
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                "section": "Related Work",
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                "text": "In the related field of opinion topic extraction, different researchers have contributed their efforts (Kobayashi et al., 2004; Nasukawa et al., 2003; Popescu & Etzioni, 2005) . Nevertheless, these works are based on lexicon look up and are applied to the domain of product reviews. The topic annotation task on the MPQA corpus is described in Stoyanov and Cardie (2008) .",
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                        "text": "(Kobayashi et al., 2004;",
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                        "start": 128,
                        "end": 150,
                        "text": "Nasukawa et al., 2003;",
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                        "text": "Popescu & Etzioni, 2005)",
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                "section": "Related Work",
                "sec_num": "2."
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                "text": "The method of identifying an opinion with its holder and topic from online news is described in Kim and Hovy (2006) . The model extracts opinion topics associated with a specific argument position for subjective expressions signaled by verbs and adjectives. Similarly, the verb based argument extraction and associated topic identification is considered Emotion Holder, and Topic 83 in the present system. Nevertheless, opinion topic identification differs from topic segmentation (Choi, 2000) . The opinion topics are not necessarily spatially coherent as there may be two opinions in the same sentence on different topics, as well as opinions on the same topic that are separated by opinions that do not share that topic (Stoyanov & Cardie, 2008) .",
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                        "text": "Holder,",
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                        "text": "and Topic 83",
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                ],
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                "section": "Related Work",
                "sec_num": "2."
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                "text": "The authors established such a hypothesis by applying the technique of co-reference identification for topic annotation. In the case of our present system, the building of fine-grained topic knowledge based on the rhetorical structure and segmentation of topics using different types of lexical, syntactic, and overlapping features substantially reduces the problem of emotion topic distinction. It must be mentioned that the proposed method obtains a moderately more reliable alpha score in comparison to some related results in Stoyanov and Cardie (2008a) .",
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                        "text": "Stoyanov and Cardie (2008a)",
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                "section": "Related Work",
                "sec_num": "2."
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                "text": "Moreover, all of the aforementioned works have been attempted for English. Recent study shows that non-native English speakers support the growing use of the Internet 1 . In addition to that, a rapidly growing number of web users from multilingual communities have focused attention on improving multilingual search engines in respect to sentiment or emotion. This raises the demand for emotion analysis for languages other than English. Bengali is the sixth most popular language in the World 2 , second in India, and the national language in Bangladesh, but it is less computerized than English. Works on emotion analysis in Bengali have started recently (Das & Bandyopadhyay, 2009a; 2010a) . The comparative evaluation of the features on equivalent domains for Bengali and English language can be found in Das and Bandyopadhyay (2009b) . To the best of our knowledge, at present, no such user-topic co-reference analysis of emotion has been conducted for Bengali or for other Indian languages. Thus, we believe that this work would meet the demands of user-topic focused emotion analysis systems.",
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                        "text": "(Das & Bandyopadhyay, 2009a;",
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                "section": "Related Work",
                "sec_num": "2."
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                "text": "A simple rule-based system has been designed to identify the emotional expression, holder, and topic from the sentences and their co-references. A simple neighboring chunk consideration approach that assumes that emotional expression, holder, and topic appear as neighboring chunks in a sentence has been introduced to identify the co-reference among the three components. The details of the system are as follows.",
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                "section": "Baseline System",
                "sec_num": "3."
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                "text": "The blog sentences are passed through an open sourced Bengali shallow parser 3 . This shallow parser gives different morphological information (root, lexical category of the root, gender, number, person, case, vibhakti, tam, suffixes, etc.) that help in identifying the lexical patterns of the emotional expressions. The shallow parsed sentences are preprocessed to generate simplified lexical patterns (as shown below). We search through each of the component words from the chunks in the Bengali WordNet Affect lists (Das & Bandyopadhyay, 2010b) . If any word present in a chunk is an emotion word (e.g. \u09c7\u0995\u09d7\u09a4\u09c1 \u0995 koutuk 'comic'), all of the words present in that extracted chunk are treated as the candidate seeds for an emotional expression. Identification of an emotional expression containing a single emotion word is straightforward. Nevertheless, we include all of the words of a chunk in order to identify long emotional expressions. Consecutive words that appear in a chunk and contain at least one emotion word also form an emotional expression. An example of a shallow parsed result follows.",
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                        "text": "(root, lexical category of the root, gender, number, person, case, vibhakti, tam, suffixes, etc.)",
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                        "text": "(Das & Bandyopadhyay, 2010b)",
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                "section": "Identifying Emotional Expression:",
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            {
                "text": "((JJP \u09b8\u09c1 n\u09b0 'sundar' [beautiful] JJ <fs af='\u09b8\u09c1 n\u09b0 ,adj,,,,d,\u09b6\u09c1 \u09a8\u09af\u09cd ,\u09b6\u09c1 \u09a8\u09af\u09cd'> ) ( NP \u09c7\u0995\u09d7\u09a4\u09c1 \u0995\u099f\u09be 'koutukta' [comic] NN <fs af=' \u09c7\u0995\u09d7\u09a4\u09c1 \u0995 ,v,,,,, \u099f\u09be , \u099f\u09be > ))",
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                "section": "Identifying Emotional Expression:",
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                "text": "In many cases, the components of a given emotional expression are separated by stop words (e.g. e\u0995\u09bf\u099f ekti 'a,' \u0990 oi 'that,' ei ei 'this'), conjunctions (e.g. e\u09ac\u0982 ebong 'and,' a\u09a5\u09ac\u09be athoba 'or,' \u09bf\u0995n kintu 'but,' etc.), negations (e.g. \u09a8\u09df noy 'not,' \u09a8\u09be na 'neither,' etc.) or intensifiers (\u09a4\u09bei tai 'so,' \u0996\u09c1 \u09ac khub 'very,' \u0995\u09ae kam 'less,' \u09c7\u09ac\u09bf\u09b6 beshi 'much'). Each emotional expression is tagged with any of Ekman's (1993) ",
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                        "text": "Ekman's (1993)",
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            },
            {
                "text": "The baseline system considers the phrasal patterns containing similarity clues to identify the emotion holders. The patterns are grouped according to part-of-speech (POS) categories. It has been observed that the hints of grouping the patterns are present mostly in the user comment portions of the Bengali blog texts. The Bengali blog structure 4 has been designed well, and each of the user comment portions starts with a corresponding username. The username is the default hint that helps in capturing the first holder present in an anchoring vector representing the nested sources. In other cases, the POS tags of the shallow parsed sentences contain similar patterns at the lexical level. The Named Entities (NEs) that are tagged with NNPC (Compound proper noun), NNP (Proper noun), NNC (Compound common noun), NN (Common noun), or PRP (Pronoun) tags at the beginning of a sentence are tagged as the possible candidates for the emotion holder. The similarity pattern consists of two phrasal constituents, the subject and the verb. The portion of the sentence excluding the subject and the verb that contains the additional constituents of the sentence has been identified as the common portion (Common_Portion). As Bengali is a free phrase order language, the order between the verb and the common portion is not fixed. The similarity patterns mostly exist in simple sentences. Such information is difficult to obtain from complex or compound sentences under this scheme; thus, the system also fails to identify nested emotion holders. A total of 59 complex sentences and 34 compound sentences were present in the test set of 503 sentences.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Identification of Emotion Holder:",
                "sec_num": null
            },
            {
                "text": "The shallow chunked texts formed by removing emotional expressions and holders were identified as the responsible spans that contain one or more potential emotion topics. Without attempting any typical strategy, the words containing only the POS tags of NNP or NNC of the shallow chunks were identified as the emotion topics.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Identifying Emotion Topic:",
                "sec_num": null
            },
            {
                "text": "The emotion topic is intended by the emotion holder, and the topic depends on the context in which its associated emotional expression occurs (Stoyanov & Cardie, 2008a) . Based on this hypothesis, each identified holder and topic that is associated with an emotional expression in a sentence is termed as co-referent if it shares the same emotional expression with the others. A rule-based technique has been adopted to identify the co-reference between the holder and topic with respect to an emotional expression if the chunks that are responsible for emotion holder or topic are the immediately neighboring chunks of that emotional expression.",
                "cite_spans": [
                    {
                        "start": 142,
                        "end": 168,
                        "text": "(Stoyanov & Cardie, 2008a)",
                        "ref_id": "BIBREF32"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Emotional Expression Holder / Topic Co-reference:",
                "sec_num": null
            },
            {
                "text": "The three identified components were stored in a vector. The evaluation of the vectors was carried out using Krippendorff's (2004) \u03b1 metric by considering each of the vectors as the unit to be analyzed. Two vectors were filled up by the emotional components that were acquired from the annotated sentences and system generated results. Considering the annotated and system generated outputs as two separate raters, we used the number of identified components as the values to be assigned for each vector. We evaluated the system through the help of inter-rater agreement and measured the performance of the system using Krippendorff's (2004) \u03b1 metric. The inter-rater agreement produces an \u03b1 score of 0.53 on the test set. This is a standard metric employed for inter-annotator reliability studies. Krippendorff's \u03b1 is a theoretically founded measure with a nice probabilistic interpretation. It was designed to measure the reliability of coding agreement, and the generalization of this metric was used as the evaluation metric for identifying co-reference in opinion topic annotation (Stoyanov & Cardie, 2008a ). Krippendorff's alpha is applicable to any number of coders (each assigning one value to one unit of analysis); to incomplete (missing) data; to any number of values available for coding a variable; and to binary, nominal, ordinal, interval, ratio, polar, and circular metrics (Levels of Measurement). In addition, it adjusts itself to small sample sizes of reliability data. We have concentrated only on nominal alpha as we have considered the strings of names.",
                "cite_spans": [
                    {
                        "start": 109,
                        "end": 130,
                        "text": "Krippendorff's (2004)",
                        "ref_id": "BIBREF22"
                    },
                    {
                        "start": 620,
                        "end": 641,
                        "text": "Krippendorff's (2004)",
                        "ref_id": "BIBREF22"
                    },
                    {
                        "start": 1086,
                        "end": 1111,
                        "text": "(Stoyanov & Cardie, 2008a",
                        "ref_id": "BIBREF32"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Evaluation:",
                "sec_num": null
            },
            {
                "text": "It is observed that some sentences may or may not contain all three emotional components. Hence, three out of four values for each of the raters are assigned based on the number of annotated or acquired emotional components from the gold standard and system-generated data, respectively. One value has been considered for undetermined cases. If no annotated or system generated emotional component is tagged or acquired, the corresponding vector unit is considered as incomplete or as containing missing data. The metric, nominal alpha, produces an \u03b1 score of 0.53 for measuring the agreement between the annotated and system generated data. The lower score of \u03b1, along with the availability of different features in the corpus, also motivated us to adopt a machine learning framework.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Evaluation:",
                "sec_num": null
            },
            {
                "text": "The Topic co-reference resolution resembles another well-known problem in NLP -the noun phrase (NP) co-reference resolution that considers machine learning frameworks (Soon et al., 2001; Ng & Cardie, 2002) . Therefore, we adopted a Support Vector Machine (SVM) (Joachims, 1998) based standard machine learning approach for identifying holder-topic co-reference from the perspective of emotional expressions, where the input vectors contain emotional expressions, holders, and topics. The training and classification processes for SVM were carried out by YamCha toolkit 5 and TinySVM-0.07 6 , respectively. The system was trained with 2234 sentences. The best feature set was identified using 630 development sentences. An Information Gain Based Pruning (IGBP) was applied to the development set, and it improved the performance of the supervised system significantly.",
                "cite_spans": [
                    {
                        "start": 167,
                        "end": 186,
                        "text": "(Soon et al., 2001;",
                        "ref_id": "BIBREF31"
                    },
                    {
                        "start": 187,
                        "end": 205,
                        "text": "Ng & Cardie, 2002)",
                        "ref_id": "BIBREF26"
                    },
                    {
                        "start": 261,
                        "end": 277,
                        "text": "(Joachims, 1998)",
                        "ref_id": "BIBREF17"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Supervised Framework",
                "sec_num": "4."
            },
            {
                "text": "Feature plays a crucial rule in the SVM framework. By manually reviewing the blog data and different language specific characteristics, word level and context level features have been selected heuristically for our classification task. The heart of our method is to give an input vector containing the emotional expression, holder, and topic, and the goal of the classifier is to determine whether the co-reference exists among the available components of the vector or not. Therefore, we have considered five different classes for identifying the co-reference between any pair of components. The classes are Expression-Holder (EH), Expression-Topic (ET), Holder-Topic (HT), Expression-Holder-Topic (EHT), and none. We use the manually annotated corpus (Das & Bandyopadhyay, 2010a) to train the classifier automatically. We construct each training example for each input vector. The co-reference identification relies on the expressiveness of the features used to describe the training example. We use the following four categories of features: lexical, syntactic, semantic, rhetorical, and overlapping features.",
                "cite_spans": [
                    {
                        "start": 753,
                        "end": 781,
                        "text": "(Das & Bandyopadhyay, 2010a)",
                        "ref_id": "BIBREF7"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Supervised Framework",
                "sec_num": "4."
            },
            {
                "text": "Parts-of-Speech (POS): We are interested in the noun, adjective, verb, and adverb words as these are emotion informative constituents. The POS features are extracted from the shallow parsed results used by the baseline system.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Lexical Features",
                "sec_num": "4.1"
            },
            {
                "text": "Negative words that are annotated in the corpus (Das & Bandyopadhyay, 2010a ) (\u09a8\u09df noy 'not,' \u09a8\u09be na 'neither,' etc.) were considered as a separate feature.",
                "cite_spans": [
                    {
                        "start": 48,
                        "end": 75,
                        "text": "(Das & Bandyopadhyay, 2010a",
                        "ref_id": "BIBREF7"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Negations (NEG):",
                "sec_num": null
            },
            {
                "text": "The Conjunctions were annotated in the emotion corpus (Das & Bandyopadhyay, 2010a) . The conjunctions were used as features (e.g. e\u09ac\u0982 ebong 'and,' a\u09a5\u09ac\u09be athoba 'or,' \u09bf\u0995n kintu 'but,' etc.) for training and testing.",
                "cite_spans": [
                    {
                        "start": 54,
                        "end": 82,
                        "text": "(Das & Bandyopadhyay, 2010a)",
                        "ref_id": "BIBREF7"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conjunctions (CONJ):",
                "sec_num": null
            },
            {
                "text": "Punctuation Symbols (Sym): Symbols, such as (,), (!), (?), are often used in single or multiple numbers to emphasize emotional expressions and are considered crucial clues for identifying emotional presence in a sentence. Thus, a special feature for such symbols was added in the active feature set for training and testing of the supervised system. Emoticons (emot_icon): Emoticons (\u263a, , ) and their consecutive occurrence generally contribute real sentiment to the emotional expressions that precede or follow them. Like punctuation symbols, emoticons were also included as a feature. A knowledge base for emoticons has been prepared by experts after minutely analyzing the Bengali blog data. Each image link of an emoticon in the raw corpus was mapped to its corresponding textual entity in the tagged corpus with its proper emotion types using the knowledge base .",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conjunctions (CONJ):",
                "sec_num": null
            },
            {
                "text": "We augment the knowledge of subcategorization frames or syntactic frames for identifying emotion holders and topics. The identification of syntactic frames is not straightforward. The detailed methodology is as follows. ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Extraction of Subcategorization Frames for Identifying Syntactic Features",
                "sec_num": "4.2"
            },
            {
                "text": "The determination of an equivalent English synset of a Bengali verb was carried out using a Bengali to English bilingual dictionary 7 . The method to extract the English equivalent synsets of the Bengali verbs was based on the work done in Banerjee et al. (2009) . We have identified the English equivalent verb synsets of the Bengali verb entries that are present in the dictionary. For example, the dictionary entries for the conjunct verb \u0986\u09a8n \u0995\u09b0\u09be ananda kara 'enjoy' are as follows.",
                "cite_spans": [
                    {
                        "start": 240,
                        "end": 262,
                        "text": "Banerjee et al. (2009)",
                        "ref_id": "BIBREF1"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "English Equivalent Synset Identification:",
                "sec_num": null
            },
            {
                "text": "< \u0986\u09a8n \u0995\u09b0\u09be v. to rejoice; to make merry.\u2026> Different synonyms for a Bengali verb having the same sense are separated using \",\" and different senses are separated using \";\" in the dictionary. The synonyms, including similar senses of the target verb, were extracted from the dictionary and yielded a set called the English Equivalent Synset (EES). In the above example, two English Equivalent Synsets (EES)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "English Equivalent Synset Identification:",
                "sec_num": null
            },
            {
                "text": "are extracted for the conjunct verb \u0986\u09a8n ananda \u0995\u09b0\u09be kara 'enjoy'.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "English Equivalent Synset Identification:",
                "sec_num": null
            },
            {
                "text": "It also has been found that each of the English Equivalent Synsets (EES) occurs in each separate class of English VerbNet (Kipper-Schuler, 2005) . VerbNet associates the semantics of a verb with its syntactic frames and combines traditional lexical semantic information, such as thematic roles and semantic predicates, with selectional restrictions. Member verbs in the same VerbNet class share common syntactic frames; thus, they are believed to have the same syntactic behavior. The VerbNet files containing member verbs and possible subcategorization frames are stored in XML file format. Hence, the XML files of VerbNet were pre-processed to build up a general list that contains all verbs, their classes, and possible subcategorization frames (primary as well as secondary). This preprocessed list was searched to extract the present subcategorization frames for each verb (e.g. love) of the English Equivalent Synsets (EES) (e.g. love) corresponding to the Bengali verb. These extracted subcategorization frames are believed to be the valid set of argument structures for the Bengali verbs (Banerjee et al., 2010) .",
                "cite_spans": [
                    {
                        "start": 122,
                        "end": 144,
                        "text": "(Kipper-Schuler, 2005)",
                        "ref_id": "BIBREF21"
                    },
                    {
                        "start": 1096,
                        "end": 1119,
                        "text": "(Banerjee et al., 2010)",
                        "ref_id": "BIBREF2"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "English Equivalent Frame Identification:",
                "sec_num": null
            },
            {
                "text": "Frame Matching: On the other hand, the shallow parsed Bengali sentences are passed through a rule based phrasal-head extraction module to identify the phrase level argument structures of the sentences corresponding to the position of the verbs. The extracted head part of every phrase from a parsed sentence is considered as a component of its sentential argument structure. If an acquired argument structure for a Bengali emotional sentence is matched with any of the available extracted frames of English VerbNet, the thematic role based holder (Experiencer, Agent, Actor, Beneficiary, etc.) and topic (Topic, Theme, Event, etc.) information associated with the English frame syntax is mapped to the appropriate slot of the acquired Bengali argument structure. Tag conversion routines were developed to transform the POS of the system generated argument structures into the POS of the VerbNet frames. The phrase level similarity between these two languages helps in identifying the subcategorization frames (Banerjee et al., 2009) . An example follows: The argument structure contains a sentential complement \"S\" started by \u09c7\u09af -je with DET type POS. The argument structure is acquired for the Bengali conjunct verb a\u09a8\u09c1 \u09ad\u09ac \u0995\u09b0\u09be anubhab kara 'feel'. One of the extracted VerbNet frame syntax containing -that type sentential complement for the equivalent English verb feel is as [<NP value=\"Experiencer\" > </VERB> < S-that (Sentential -that Complement)>]. As the acquired argument structure matches the extracted VerbNet frame syntax, the holder related roles (e.g. Experiencer) associated with the VerbNet frame was mapped to the equivalent phrase in the acquired argument structure of the Bengali sentence. The phrase (\u09b0\u09be\u09c7\u09b6\u09a6) is now considered as a candidate of emotion holder. Additionally, the sentential complement portion is also passed through the syntactic model for obtaining any implicit emotion holders. The case markers in Bengali are required to identify the emotion holders as the case markers give the useful hints to capture the selectional restrictions that play a key role in distinguishing the emotion holders from other valid alternatives.",
                "cite_spans": [
                    {
                        "start": 547,
                        "end": 593,
                        "text": "(Experiencer, Agent, Actor, Beneficiary, etc.)",
                        "ref_id": null
                    },
                    {
                        "start": 604,
                        "end": 631,
                        "text": "(Topic, Theme, Event, etc.)",
                        "ref_id": null
                    },
                    {
                        "start": 1009,
                        "end": 1032,
                        "text": "(Banerjee et al., 2009)",
                        "ref_id": "BIBREF1"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "English Equivalent Frame Identification:",
                "sec_num": null
            },
            {
                "text": "\u09b0\u09be\u09c7\u09b6\u09a6",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "English Equivalent Frame Identification:",
                "sec_num": null
            },
            {
                "text": "Emotion/Affect Words (EW): The presence of a word in the Bengali WordNet Affect lists (Das & Bandyopadhyay, 2010b) identifies the emotion/affect words. The tagged affect words are considered as both lexical and semantic features in the case of handling the emotional expressions.",
                "cite_spans": [
                    {
                        "start": 86,
                        "end": 114,
                        "text": "(Das & Bandyopadhyay, 2010b)",
                        "ref_id": "BIBREF8"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Features",
                "sec_num": "4.3"
            },
            {
                "text": "The Bengali SentiWordNet was developed by replacing each word entry in the synonymous set of the English SentiWordNet (Esuli & Sebastiani, 2006) by its possible",
                "cite_spans": [
                    {
                        "start": 118,
                        "end": 144,
                        "text": "(Esuli & Sebastiani, 2006)",
                        "ref_id": "BIBREF14"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Intensifiers (INTF):",
                "sec_num": null
            },
            {
                "text": "Bengali synsets using the English to Bengali bilingual dictionary that was developed as part of the EILMT project 8 . The chunks containing JJ (adjective) and RB (adverb) tagged elements were considered to be intensifiers. If the intensifier was found in the SentiWordNet, then the positive and negative scores of the intensifier were retrieved from the SentiWordNet. The intensifier is classified into the list of positive (pos) (INTFpos) or negative (neg) (INTFneg), for which the average retrieved score is higher. The intensifiers play an important role in identifying the lexical association among the component words of an emotional expression and linking the emotion components based on their POS. ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Intensifiers (INTF):",
                "sec_num": null
            },
            {
                "text": "The present task acquires the rhetorical components, such as locus, nucleus, and satellite (Mann & Thompson, 1988) , from a sentence, as these rhetorical clues help in identifying the individual topic spans. The part of the text span containing an annotated emotional expression is considered as locus. Primarily, the separation of nucleus from satellite is done based on the punctuation marks (,), (!), (?). Frequently used discourse markers (\u09c7\u09af\u09c7\u09b9\u09a4\u09c1 jehetu 'as,' \u09c7\u09af\u09ae\u09a8 jemon 'e.g.,' \u0995\u09be\u09b0\u09a3 karon 'because,' \u09ae\u09be\u09c7\u09a8 mane 'means' ) and causal verbs (\u0998\u099f\u09be\u09df ghotay 'caused') also act as useful clues if they are explicitly specified in the text. Stoyanov and Cardie (2008a) mentioned that the topic depends on the context in which its associated emotional expression occurs. If any word of an emotional expression co-occurs with any word element of the nucleus or satellite in the same shallow chunk, the feature is considered a common rhetoric similarity. Otherwise, the feature is considered a distinctive rhetoric similarity. The features aim to separate emotion topics from non-emotion topics as well as the individual topic from an overlapped topic region.",
                "cite_spans": [
                    {
                        "start": 91,
                        "end": 114,
                        "text": "(Mann & Thompson, 1988)",
                        "ref_id": "BIBREF23"
                    },
                    {
                        "start": 636,
                        "end": 663,
                        "text": "Stoyanov and Cardie (2008a)",
                        "ref_id": "BIBREF32"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Rhetoric Features",
                "sec_num": "4.4"
            },
            {
                "text": "Word Overlap: This feature is true if any two topic spans contain a common word.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Overlapping Features",
                "sec_num": "4.5"
            },
            {
                "text": "The verb, noun, adjective, and adverb are considered as overlapping informative constituents.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Part-of-Speech Overlap:",
                "sec_num": null
            },
            {
                "text": "This binary feature is True if the two chunks contain NPs that are determined to be co-referent by applying a rule of common rhetoric similarity.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "NP Co-reference:",
                "sec_num": null
            },
            {
                "text": "Each of the sentences is passed through a Named Entity Recognizer (Ekbal & Bandyopadhyay, 2008) to identify the named entities in that sentence.",
                "cite_spans": [
                    {
                        "start": 66,
                        "end": 95,
                        "text": "(Ekbal & Bandyopadhyay, 2008)",
                        "ref_id": "BIBREF12"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Named Entity (NE):",
                "sec_num": null
            },
            {
                "text": "If any word is tagged as a named entity (NE), a feature is assigned for either emotion holder or topic. If, however, the word is present in satellite and not tagged as an emotion holder (EH) feature, the word is selected as a potential candidate for topic. This distinguishing feature is considered for identifying the holder and topic separately from an NE overlapped context.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Named Entity (NE):",
                "sec_num": null
            },
            {
                "text": "The combination of multiple features in comparison with a single feature generally shows a reasonable performance enhancement of any classification system. The impact of different features and their combinations was measured on the development set of 630 sentences. Different unigram and bi-gram context features (word and POS tag level) and their combinations were generated from the training corpus as well. We added each feature into the active feature list one at a time to see if the inclusion of a feature in the existing feature set improved the F-Score of the system on the development set. The final active feature set was applied to the test data. During the SVM-based training phase, the current token word with the three previous and three following words and their corresponding POS, along with negation or intensifier, were selected as context features for that word. We used Krippendorff's (2004) alpha (as discussed in Section 3) for measuring the performance of the system. The importance of incorporating the features was examined through Information Gain (InfoGain). All of the results were obtained by the 10 fold cross validation method.",
                "cite_spans": [
                    {
                        "start": 890,
                        "end": 911,
                        "text": "Krippendorff's (2004)",
                        "ref_id": "BIBREF22"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Experimental Results",
                "sec_num": "5."
            },
            {
                "text": "This decision technique was used to measure the importance of a feature (X) with respect to the class attribute (Y). Formally, the information gain of a feature X with respect to a class attribute Y is the reduction in uncertainty about the value of Y when we know the value of X:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "InfoGain (Y;X) = entropy(Y) -entropy (Y|X) where X and Y are discrete variables taking values {x 1 , x 2 ,....,x m } and {y 1 ,y 2 ,....,y n }, respectively. The Entropy(Y) is defined as:",
                "cite_spans": [
                    {
                        "start": 37,
                        "end": 42,
                        "text": "(Y|X)",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "Entropy (Y) = -\u2211 i=1 to n P(Y= yi ) log 2 P(Y= yi )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "The conditional entropy of Y given X is defined as:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "Entropy (Y|X) = -\u2211 i=1 to m P(X= xj ) Entropy (Y|X= xj )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "The features with high Information Gain (InfoGain) reduce the uncertainty about a class to the minimum. In our experiment on the development set, all of the features except the features for the causal verbs and distinctive rhetoric similarity achieved a high Information Gain (InfoGain). The word features (e.g. non-emotional words, such as gather, seem, etc.) were not considered based on a threshold of 0.5.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "The metric, nominal alpha produced an \u03b1 score of 0.53 between the annotated and system generated data. Generally, the alpha \u03b1 score aims to probabilistically capture the agreement of annotated data and separate it from the chance of agreement. The baseline score achieved for the overall agreement was 0.53, which is below the generally accepted level, while \u03b1 for the supervised system was 0.63, which is moderately acceptable and reliable. The scores of \u03b1 for the baseline system and supervised system, along with some important features, their combinations, and pruning steps, are shown in Table 1 . The \u03b1 score loses its probabilistic interpretation due to the way it is adapted to the problem of co-reference classification. It is observed that the score of \u03b1 increased rapidly while considering the syntactic, rhetorical, and overlapping features. The overlapping features also cause problems because of the free phrase order characteristics of the Bengali language. The overlapping context of emotional expression and topic generates errors. Nevertheless, the application of Named Entities NEsreduces the problem of distinguishing holder and topic. ",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 593,
                        "end": 600,
                        "text": "Table 1",
                        "ref_id": "TABREF4"
                    }
                ],
                "eq_spans": [],
                "section": "Information Gain Based Pruning (IGBP):",
                "sec_num": null
            },
            {
                "text": "The error analysis was conducted on the development set of 630 sentences. We incorporated different rule-based post processing techniques for handling the error cases, and the system achieved an alpha score of 0.67. Four types of error cases were identified, and four different rules were proposed to reduce the error cases.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Error Analysis",
                "sec_num": "6."
            },
            {
                "text": "Emotion Holder, and Topic 93 Case 1: Appositive Use: The implicit emotion holders may be present in a sentence. (e.g.",
                "cite_spans": [
                    {
                        "start": 8,
                        "end": 15,
                        "text": "Holder,",
                        "ref_id": null
                    },
                    {
                        "start": 16,
                        "end": 28,
                        "text": "and Topic 93",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Error Analysis",
                "sec_num": "6."
            },
            {
                "text": "\u09b0\u09be\u09ae 'Ram' in the case of \u09b0\u09be\u09c7\u09ae\u09b0 \u09b8\u09c1 \u0996 'Ram's pleasure'). The identification of the emotion holder at the sentence level requires the knowledge of two basic constraints (implicit and explicit) separately. The explicit constraints identify the single prominent emotion holder that is directly involved with the emotional expression, whereas the implicit constraints identify all direct and indirect nested sources as emotion holders. ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Error Analysis",
                "sec_num": "6."
            },
            {
                "text": "We considered the suffixes that are determined from the shallow parsed phrases to identify the appositive cases. In the above example, the appositive case (e.g. \u09b0\u09be\u09c7\u09ae\u09b0 \u09b8\u09c1 \u0996 (Ram's pleasure)) is also identified and placed in the vector by removing the inflectional suffix (-e\u09b0 -er in this case). Sometimes, the vibhakti and tam information also play effective roles in identifying emotion holders.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Solution:",
                "sec_num": null
            },
            {
                "text": "Case 2: Anaphoric Presence of Holders: Another similar problem is identified in the above example. The emotion holders are sometimes referred to via anaphors. Sometimes, the candidate anaphors are linked with the emotional expressions instead of the actual emotion holders. The actual emotion holder \u09c7\u0997\u09a6\u09c1 \u099a\u09be\u099a\u09be 'Gedu ChaCha' expresses the emotion in a clause that is represented by the anaphor \u0986\u09bf\u09ae ami 'I' in another clause.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Solution:",
                "sec_num": null
            },
            {
                "text": "The sentences of user comments in the adopted blog corpus contain a special default phrasal pattern that helps in identifying the emotion holders ([<Named Entity> <say>] e.g. \u09c7\u0997\u09a6\u09c1 \u099a\u09be\u099a\u09be \u09ac\u09c7\u09b2: (Gedu ChaCha bole), \u09b0\u09be\u09c7\u09b6\u09a6 \u09ac\u09c7\u09b2\u09c7\u099b\u09a8 (Rashed bolechen), and \u09b8\u09be\u09df\u09a8 \u09ac\u09c7\u09b2\u09c7\u099b (Sayan bolechhe)).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Solution:",
                "sec_num": null
            },
            {
                "text": "Hence, if a pronoun is present with an emotional expression, the preceding Named Entities of such a default phrasal pattern are considered as the emotion holders.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Solution:",
                "sec_num": null
            },
            {
                "text": "The complex or compound sentences contain more than one clause, and each of the clauses may contain individual emotional expressions. The holders and topics associated with the emotional expressions in all of the clauses need fine-grained study of the sentential structures. The following example shows that two emotional expressions (\u09a6\u09c1 \u0983\u0996 dookkha 'sorrow' and \u0986\u09a8n ananda 'happy') contain two different Solution: As the complex or compound sentences contain more than one clause and each of the clauses contains individual emotional expressions, we consider the sentential rhetorical structure. Instead of identifying rhetorical relations (Mann & Thompson, 1988) , the present task acquires the rhetorical components, such as locus, nucleus, and satellite from a sentence, as these rhetoric clues help in identifying the individual holder and topics associated in each clause of the sentence. The part of the text span containing the emotional expression is considered as locus. Primarily, the separation of nucleus from satellite is done based on the punctuation marks (,), (!), (?). Frequently used discourse markers (\u09c7\u09af\u09c7\u09b9\u09a4\u09c1 jehetu 'as,' \u09c7\u09af\u09ae\u09a8 jemon 'e.g.,' \u0995\u09be\u09b0\u09a3 karon 'because,' \u09ae\u09be\u09c7\u09a8 mane 'means') and causal verbs (\u0998\u099f\u09be\u09df ghotay 'caused') also are useful clues if they are explicitly specified in the text and present in a manually prepared seed list. If any word in the emotional expression co-occurs with any word element of the nucleus or satellite in the same chunk, the feature is considered a common rhetoric similarity.",
                "cite_spans": [
                    {
                        "start": 640,
                        "end": 663,
                        "text": "(Mann & Thompson, 1988)",
                        "ref_id": "BIBREF23"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Case 3: Multiple Holders and Topics:",
                "sec_num": null
            },
            {
                "text": "Otherwise, the feature is considered a distinctive rhetoric similarity. The chunks identified by the syntactic system as the holder and topic and tagged as common rhetoric similarity are only considered for each of the clauses of a sentence. For this reason, all possible holders and topics associated to all of the clauses of a sentence are identified by the syntactic system. Case 4: Overlapping Topic Spans: It is observed that the emotion topics containing single word tokens are identified more easily than multi word topics. Sometimes, the emotion related topics coexist with other potential non-emotional topics. As the topics may consist of multi-word strings, the text spans denoting the topic spans create problems in identifying emotion topic span from other non-emotional topic spans. In the following example, the emotional expression \u0986\u09a8n ananda 'enjoy' is related to the topic \u0997\u09be\u09a8 gan 'song' and \u09bf\u099f\u09bf\u09ad TV 'television'. The baseline system additionally captures \u09aci boi 'book' that is a potential but non-emotion topic.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Case 3: Multiple Holders and Topics:",
                "sec_num": null
            },
            {
                "text": "Emotion Holder, and Topic 95 \u09a4\u09c1 \u09bf\u09ae \u09c7\u09a4\u09be \u09aci \u09aa\u09dc\u09c7\u09a4i \u09a8\u09be, e\u0996\u09a8 \u09c7\u09a6\u0996\u09bf\u099b \u09a4\u09c1 \u09bf\u09ae \u0997\u09be\u09a8, \u09bf\u099f\u09bf\u09ad \u09c7\u09a4o (tumi) (to) (boi) (portei) (na), (ekhon) (dekhchi) (tumi) (gan), (TV) (teo) \u0986\u09a8n \u09aa\u09beo\u09a8\u09be\u0964 (ananda) (paona)",
                "cite_spans": [
                    {
                        "start": 8,
                        "end": 15,
                        "text": "Holder,",
                        "ref_id": null
                    },
                    {
                        "start": 16,
                        "end": 28,
                        "text": "and Topic 95",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Case 3: Multiple Holders and Topics:",
                "sec_num": null
            },
            {
                "text": "You never used to read books; now we notice that you also don't enjoy song/ television.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Case 3: Multiple Holders and Topics:",
                "sec_num": null
            },
            {
                "text": "The topic of an opinion depends on the context in which its associated opinion expression occurs (Stoyanov & Cardie, 2008a) . The common rhetoric similarity feature helps the syntactic system by aiming to separate emotion topics from non-emotion topics and to separate the overlapping possibilities of discrete emotion topic spans from non-topical contiguous regions. If the identified topic chunks are tagged with common rhetoric similarity, the chunks are classified as emotional topics and separated from non-topical elements in a sentence. The improvements at some important steps by incorporating the rule based post-processing techniques are shown in Table 2 . It is observed that the simple rules have substantially reduced errors and have improved the performance of the system satisfactorily. The application of the post-processing techniques also achieves an alpha score of 0.6721 on the test set. ",
                "cite_spans": [
                    {
                        "start": 97,
                        "end": 123,
                        "text": "(Stoyanov & Cardie, 2008a)",
                        "ref_id": "BIBREF32"
                    }
                ],
                "ref_spans": [
                    {
                        "start": 657,
                        "end": 664,
                        "text": "Table 2",
                        "ref_id": "TABREF7"
                    }
                ],
                "eq_spans": [],
                "section": "Solution:",
                "sec_num": null
            },
            {
                "text": "The automatic extraction of emotional expressions, sentential emotion holders, and topics from Bengali blog data is accomplished in the present task. The supervised implementation of the system shows improvement over the rule-based baseline because the rule-based system fails to capture the implicit textual clues whereas the supervised system captures the clues in terms of combined features. The evaluation of the co-reference using Krippendorff's alpha is helpful in diagnosing the importance of the three emotional components. The rule-based post-processing techniques for reducing the error cases have shown substantial improvement in the performance of the system. From the overall analysis, it is observed that the identification of emotional co-reference is helpful in identifying user-topic relations. The handling of metaphors and their impact in detecting sentence level emotion is not considered. Future analysis concerning the time based emotional change can be used for topic model representation. The need for co-reference requires that the presence of indirect affective clues can also be traced with the help of the holder and topic.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusion",
                "sec_num": "7."
            },
            {
                "text": "http://www.internetworldstats.com/stats.htm 2 http://www.ethnologue.com/ethno_docs/distribution.asp?by=size 3 http://ltrc.iiit.ac.in/showfile.php?filename=downloads/shallow_parser.php",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "www.amarblog.com/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "http://chasen-org/~taku/software/yamcha/ 6 http://chasen.org/~taku/software/TinySVM/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "http://home.uchicago.edu/~cbs2/banglainstruction.html",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "English to Indian Languages Machine Translation (EILMT) is a TDIL project undertaken by the consortium of different premier institutes and sponsored by MCIT, Govt. of India.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [],
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            },
            "TABREF5": {
                "content": "<table><tr><td colspan=\"3\">Holder: &lt; \u09c7\u0997\u09a6\u09c1 \u099a\u09be\u099a\u09be, \u09a8\u09be\u09b8\u09bf\u09b0\u09a8 \u09b8\u09c1 \u09b2\u09a4\u09be\u09a8\u09be &gt;</td></tr><tr><td>\u09c7\u0997\u09a6\u09c1 \u099a\u09be\u099a\u09be</td><td colspan=\"2\">\u09ac\u09c7\u09b2, \u09a8\u09be \u09c7\u0997\u09be \u09c7\u09ac\u09be\u09a8 , \u0986\u09bf\u09ae</td><td>\u09a8\u09be\u09b8\u09bf\u09b0\u09a8 \u09b8\u09c1 \u09b2\u09a4\u09be\u09a8\u09be\u09b0</td></tr><tr><td colspan=\"4\">(Gedu ChaCha) (bole) : (na) (go) (bon) , (ami)</td><td>(Nasreen Sultanar)</td></tr><tr><td>\u09a6\u09c1 \u0983\u09c7\u0996\u09b0</td><td>\u0995\u09a5\u09be\u09c7\u09a4</td><td>\u09c7\u0995\u0981 \u09c7\u09a6 \u09c7\u09ab\u09bf\u09b2 \u0964</td></tr><tr><td colspan=\"3\">(dookher) (kathate) (kende) (feli)</td></tr></table>",
                "num": null,
                "type_str": "table",
                "text": "The following example contains the emotion holder \u09a8\u09be\u09b8\u09bf\u09b0\u09a8 \u09b8\u09c1 \u09b2\u09a4\u09be\u09a8\u09be (Nasreen Sultana) based on implicit constraints.",
                "html": null
            },
            "TABREF6": {
                "content": "<table><tr><td>(Gedu ChaChar) (dookkha) (thaka) (satweo) (Chachi) (ananda) (kare) (sabaike)</td></tr><tr><td>\u09bf\u09a8\u09c7\u09df \u09a5\u09be\u09c7\u0995 \u0964</td></tr><tr><td>(niye) (thake)</td></tr><tr><td>Though Gedu Chacha has sorrow, Chachi lives happily with all.</td></tr></table>",
                "num": null,
                "type_str": "table",
                "text": "94 Dipankar Das & Sivaji Bandyopadhyay holders (\u09c7\u0997\u09a6\u09c1 \u099a\u09be\u099a\u09be Gedu ChaCha and \u099a\u09be\u09bf\u099a Chachi). \u09c7\u0997\u09a6\u09c1 \u099a\u09be\u099a\u09be\u09b0 \u09a6\u09c1 \u0983\u0996 \u09a5\u09be\u0995\u09be \u09b8\u09c7tto \u099a\u09be\u09bf\u099a \u0986\u09a8n \u0995\u09c7\u09b0 \u09b8\u09ac\u09bei\u09c7\u0995",
                "html": null
            },
            "TABREF7": {
                "content": "<table><tr><td>Cases</td><td>Krippendorff's \u03b1</td></tr><tr><td>Before Error Analysis</td><td>0.6332</td></tr><tr><td>Case 1</td><td>0.6476</td></tr><tr><td>Case 2</td><td>0.6417</td></tr><tr><td>Case 3</td><td>0.6498</td></tr><tr><td>Case 4</td><td>0.6402</td></tr><tr><td>Case 1+ Case 2</td><td>0.6510</td></tr><tr><td>Case 1+Case 3</td><td>0.6533</td></tr><tr><td>Case 1+Case 2+Case 3</td><td>0.6601</td></tr><tr><td>Case 1+Case 2+Case 4</td><td>0.6625</td></tr><tr><td>Case 1+Case 3+Case 4</td><td>0.6678</td></tr><tr><td>Case 1+Case 2+Case 3+Case 4</td><td>0.6772</td></tr></table>",
                "num": null,
                "type_str": "table",
                "text": "",
                "html": null
            }
        }
    }
}