| { | |
| "paper_id": "2020", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T07:29:10.980424Z" | |
| }, | |
| "title": "Software to Extract Parallel Data from English-Punjabi Comparable Corpora", | |
| "authors": [ | |
| { | |
| "first": "Manpreet", | |
| "middle": [], | |
| "last": "Singh Lehal", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Lyallpur Khalsa College", | |
| "location": { | |
| "settlement": "Jalandhar" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Ajit", | |
| "middle": [], | |
| "last": "Kumar", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Multani Mal Modi College", | |
| "location": { | |
| "settlement": "Patiala" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Vishal", | |
| "middle": [], | |
| "last": "Goyal", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Punjabi University", | |
| "location": { | |
| "settlement": "Patiala" | |
| } | |
| }, | |
| "email": "vishal.pup@gmail.com" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Machine translation from English to Indian languages is always a difficult task due to the unavailability of a good quality corpus and morphological richness in the Indian languages. For a system to produce better translations, the size of the corpus should be huge. We have employed three similarity and distance measures for the research and developed a software to extract parallel data from comparable corpora automatically with high precision using minimal resources. The software works upon four algorithms. The three algorithms have been used for finding Cosine Similarity, Euclidean Distance Similarity and Jaccard Similarity. The fourth algorithm is to integrate the outputs of the three algorithms in order to improve the efficiency of the system.", | |
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| "paper_id": "2020", | |
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| "abstract": [ | |
| { | |
| "text": "Machine translation from English to Indian languages is always a difficult task due to the unavailability of a good quality corpus and morphological richness in the Indian languages. For a system to produce better translations, the size of the corpus should be huge. We have employed three similarity and distance measures for the research and developed a software to extract parallel data from comparable corpora automatically with high precision using minimal resources. The software works upon four algorithms. The three algorithms have been used for finding Cosine Similarity, Euclidean Distance Similarity and Jaccard Similarity. The fourth algorithm is to integrate the outputs of the three algorithms in order to improve the efficiency of the system.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "Machine translation from English to Indian languages is always a difficult task due to the unavailability of a good quality corpus and morphological richness in the Indian languages. For a system to produce better translations, the size of the corpus should be huge. In addition to that, the parallel sentences should convey similar meanings, and the sentences should cover different domains. Modelling the system with such a corpus can assure good translations while testing the model. Since English -Punjabi language pair is an under-resourced pair, this study provides a breakthrough in acquiring English -Punjabi Corpus for performing the task of machine translation. We have employed Statistical methods for the research and developed a software to extract parallel data from comparable corpora automatically with high precision using minimal resources.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "We generate an English-Punjabi Comparable Corpora which is used as input data. We have used the articles from Wikipedia which are stored in the dump. The articles of English and Punjabi languages are extracted, aligned and refined. We also received access to the database of Indian Language Technology Proliferation and Deployment Centre (TDIL) and used the noisy parallel sentences. Sentences were also collected from Gyan Nidhi corpus and reports of college activities. Thus, our data is not restricted to one particular domain.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "We employ three similarity measures of Cosine Similarity, Jaccard Distance and Euclidean Distance to find the similarity of two English Corpora. Firstly, the algorithms are performed individually and then the integrated approach is used by combining the results of all the three similarity measuring algorithms to reach better output levels. The software works upon four algorithms. The three algorithms have been used for finding Cosine Similarity, Euclidean Distance Similarity and Jaccard Similarity. The fourth algorithm is to integrate the outputs of the three algorithms in order to improve the efficiency of the system. The codes for similarity algorithms have been implemented in python using Scikit Learn. The sentences are first converted into vectors using tf-idf vectorization and then the algorithms are employed.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Every similarity measure has its own limitations when used individually. Combining the scores of three similarity measures complements the features and give better results. Only those translation pairs are selected which are similarly paired in all the three algorithms. The translation pairs which do not occur in the output of one or two algorithms are discarded.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "We run the three similarity algorithms and obtain similarity scores. Threshold values are fixed by getting the average of all the similarity scores obtained for each algorithm. In case of Euclidean Distance and Jaccard Distance, the translation pairs having similarity scores below the threshold values are selected and in case of Cosine similarity translation pairs with similarity scores above the threshold value are selected. The remaining translation pairs are filtered out. This refines the output to a great extent.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "The results obtained from the three algorithms are integrated aiming at the improvement of the output translation pairs and finding the best pairs. Only those translation pairs are selected which are similarly paired in all the three algorithms. The translation pairs which do not occur in the output of one or two algorithms are discarded.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "With the integrated approach, we are able to achieve a precision level of 93 percent and accuracy is 86 percent. The results make it clear that the integrated approach improve the results to a great extent and thus, validate the usage of this approach.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "There are three components of the web interface of the software: Punjabi Input, English Input and Aligned Data. The input data in form of sentences or paragraphs is copied in relevant language boxes on the left side and submitted. It gives the translation pair output in the Aligned data box on the right-side. The tuning button is used to identify translations at the required level of similarity. It can be increased or decreased to find exact parallel sentences as well as translations pairs similar at phrase level.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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