{ "paper_id": "O12-1031", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:03:08.127401Z" }, "title": "Applications of Parallel Corpora for Chinese Segmentation", "authors": [ { "first": "Jui-Ping", "middle": [], "last": "Wang", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Chengchi University", "location": {} }, "email": "" }, { "first": "Chao-Lin", "middle": [], "last": "Liu", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Chengchi University", "location": {} }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Instead of directly providing the service of Chinese segmentation, some open-source software allows us to train segmentation models with segmented text. The resulting models can perform quite well, if training data of high quality are available. In reality, it is not easy to obtain sufficient and excellent training data, unfortunately. We report an exploration of using parallel corpora and various lexicons with techniques of identifying unknown words and near synonyms to automatically generate training data for such open-source software. We achieved promising results of segmentation in current experiments. Although the results fell short of outperforming the well-known Chinese segmenters, we believe that the proposed approach offers a viable alternative for users of the open-source software to generate their own training data.", "pdf_parse": { "paper_id": "O12-1031", "_pdf_hash": "", "abstract": [ { "text": "Instead of directly providing the service of Chinese segmentation, some open-source software allows us to train segmentation models with segmented text. The resulting models can perform quite well, if training data of high quality are available. In reality, it is not easy to obtain sufficient and excellent training data, unfortunately. We report an exploration of using parallel corpora and various lexicons with techniques of identifying unknown words and near synonyms to automatically generate training data for such open-source software. We achieved promising results of segmentation in current experiments. Although the results fell short of outperforming the well-known Chinese segmenters, we believe that the proposed approach offers a viable alternative for users of the open-source software to generate their own training data.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "(combination ambiguity) (overlapping ambiguity) ABC AB/C A/BC A B C AB BC B ABC Li [17] unsupervised Chen [11] (corpus-based learning approach)", "cite_spans": [ { "start": 83, "end": 87, "text": "[17]", "ref_id": "BIBREF7" }, { "start": 106, "end": 110, "text": "[11]", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "1", "sec_num": null }, { "text": "Chen [12] (likelihood ratios)", "cite_spans": [ { "start": 5, "end": 9, "text": "[12]", "ref_id": "BIBREF2" } ], "ref_spans": [], "eq_spans": [], "section": "1", "sec_num": null }, { "text": "2. 3.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "1", "sec_num": null }, { "text": "[8] 138", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "1", "sec_num": null }, { "text": "[6]", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "1", "sec_num": null }, { "text": "[1] Dr.eye [13] [7] E-HowNet [14] E-HowNet", "cite_spans": [ { "start": 11, "end": 15, "text": "[13]", "ref_id": null }, { "start": 29, "end": 33, "text": "[14]", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "1", "sec_num": null }, { "text": "4.1", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.", "sec_num": null }, { "text": "C 1 n C 1 n = C 1 C 2\u2026 C n V i Cand i i=1 to n V i V i C i Cand i", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.", "sec_num": null }, { "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.", "sec_num": null }, { "text": "Cand i C 1 i 3(b) 3(b) Cand i 1 \u2026 V C i V i V i 3 i C 1 i i 1 3(a) V 1 C 1 1 V 1 C 1 1 Cand 1 i 2 3(a) V 1 C 1 2 Cand 2 3(b) Cand 2 3 4 i 6 3(b) Cand 5 C 1 i C 1 : C 2 : C 3 : C 4 : C 5 : C 6 : C 7 : V i V 1 V 2 V 3 V 4 V 5 V 6 V 7 V i C i V i 1. C i i=1 to n 1 to n-i+1 V i 2. i 1 3. (a). V 1 C 1 i Cand i (b). for j =1 to i-1, i > 1 Cand j V j+1 C 1 i Cand i 4. i n i 1 3 i n Cand i Cand 6 Cand 4 V 5 C 1 6 Cand Cand 2 V C 1 6 Cand 6 3 4 i 7 Cand 7 Cand i 4.2 / / / / chance / / ABC A B C ABC A/BC AB/C AB AB/C A/BC A/BC / / 1 2 1 2 \"even\" 3 < / > < even/ > \"even\" \"even\" 1 3 / / 4.3 4.3.1 i=1 i=2 i=3 i=4 i=5 i=6 i=7", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.", "sec_num": null }, { "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Word Identification for Mandarin Chinese Sentences", "authors": [ { "first": "Shing-Huan", "middle": [], "last": "Keh-Jiann Chen", "suffix": "" }, { "first": "", "middle": [], "last": "Liu", "suffix": "" } ], "year": 1992, "venue": "Proceedings of the 15th International Conference on 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