| { | |
| "paper_id": "O03-5005", | |
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| "abstract": "WordNet provides plenty of lexical meaning; therefore, it is very helpful in natural language processing research. Each lexical meaning in Princeton WordNet is presented in English. In this work, we attempt to use a bilingual dictionary as the backbone to automatically map English WordNet to a Chinese form. However, *", | |
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| "text": "WordNet provides plenty of lexical meaning; therefore, it is very helpful in natural language processing research. Each lexical meaning in Princeton WordNet is presented in English. In this work, we attempt to use a bilingual dictionary as the backbone to automatically map English WordNet to a Chinese form. However, *", | |
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| "section": "Abstract", | |
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| "text": "we encounter many barriers between the two different languages when we observe the preliminary result for the linkage between English WordNet and the bilingual dictionary. This mapping causes the Chinese translation of the English synonym collection (Synset) to correspond to unstructured Chinese compound words, phrases, and even long string sentence instead of independent Chinese lexical words. This phenomenon violates the aim of Chinese WordNet to take the lexical word as the basic component. Therefore, this research will perform further processing to study this phenomenon.", | |
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| "text": "The objectives of this paper are as follows: First, we will discover core lexical words and characteristic words from Chinese compound words. Next, those lexical words will be expressed by means of conceptual representations. For the core lexical words, we use grammar structure analysis to locate such words. For characteristic words, we use sememes in HowNet to represent their lexical meanings. Certainly, there exists a problem of ambiguity when Chinese lexical words are translated into their lexical meanings. To resolve this problem, we use lexical parts-of-speech and hypernyms of WordNet to reduce the lexical ambiguity.", | |
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| "text": "We experimented on nouns, and the experimental results show that sense disambiguation could achieve a 93.8% applicability rate and a 93.5% correct rate.", | |
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| "text": "WordNet [Gomez, 1998] (Lexical Relation) (Conceptual Relation) WordNet [Miller, 1990; Fellbaum, 1998 ] [Farreres, Rigau and Rodriguez, 1998] WordNet [Gonzalo et al., 1998; Mandala, Tokunaga and Tanaka, 1998 ] [Knight and Luk, 1994 ] [Jing, 1998] [ Aslandogam et al., 1997 ] WordNet WordNet EuroWordNet [Atserias et al., 1997; Farreres, Rigau, and Rodriguez, 1998 ] WordNet 2. [Gale, Church and Yarowsky, 1992; Yarowsky, 1992 Yarowsky, , 1995 Resnik, 1993; Dagan and Itai, 1994; Luk, 1995; Ng and Lee, 1996; Riloff and Jones, 1999] [ Guthrie et al., 1991; Slator, 1991; Li, Szpakowicz and Matwin, 1995; Chang, 1998, Yang and Ker, 2002] [ Chang, Ker and Chen, 1998; Chen and Chang, 1998; Dorr et al., 2000; Carpuat et al., 2002; Wang, 2002 ] WordNet ] WordNet HowNet [Dorr et al., 2000; Carpuat et al., 2002 ] [ Chang, Ker and Chen, 1998; Chen and Chang, 1998] 3. 4.", | |
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| "text": "[Gomez, 1998]", | |
| "ref_id": "BIBREF13" | |
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| { | |
| "start": 71, | |
| "end": 85, | |
| "text": "[Miller, 1990;", | |
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| }, | |
| { | |
| "start": 86, | |
| "end": 100, | |
| "text": "Fellbaum, 1998", | |
| "ref_id": "BIBREF11" | |
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| "end": 140, | |
| "text": "[Farreres, Rigau and Rodriguez, 1998]", | |
| "ref_id": "BIBREF10" | |
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| "end": 171, | |
| "text": "[Gonzalo et al., 1998;", | |
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| "text": "Mandala, Tokunaga and Tanaka, 1998", | |
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| "text": "[Jing, 1998]", | |
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| "text": "Farreres, Rigau, and Rodriguez, 1998", | |
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| "text": "Resnik, 1993;", | |
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| "text": "Dagan and Itai, 1994;", | |
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| "text": "Luk, 1995;", | |
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| "text": "Slator, 1991;", | |
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| "text": "Li, Szpakowicz and Matwin, 1995;", | |
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| "end": 634, | |
| "text": "Ker, 2002]", | |
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| "end": 663, | |
| "text": "Chang, Ker and Chen, 1998;", | |
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| "start": 664, | |
| "end": 685, | |
| "text": "Chen and Chang, 1998;", | |
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| "text": "Dorr et al., 2000;", | |
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| "text": "Carpuat et al., 2002;", | |
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| "text": "Chang, Ker and Chen, 1998;", | |
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| "section": "GUM, CYC, ONTOS, MICROKOSMOS, EDR", | |
| "sec_num": null | |
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| { | |
| "text": "Dice [Dice, 1945] ", | |
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| "text": "W 1 , W 2 Sim(W 1 , W 2 ) | ) ( | | ) ( | | ) ( ) ( | 2 ) , ( 2 1 2 1 2 1 W S W S W S W S W W Sim + \u2229 \u00d7 = 5. 5.1 WordNet WordNet S n W 1 , W 2 , \u2026, W n , W i k D i1, D i2, \u2026 , D ik E ij W i D ij S E ij D ij S CDEF(S) , | | max arg ) ( CDEF , , E S S j i j i \u2229 = k j n i ,..., 1 , ..., , 1 = = \u2200 i n S k i S i W i E i, j W i D", | |
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