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- Publisher Website: 10.3115/v1/d14-1122
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Conference Paper: Dependency parsing for weibo: An efficient probabilistic logic programming approach
Title | Dependency parsing for weibo: An efficient probabilistic logic programming approach |
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Authors | |
Issue Date | 2014 |
Citation | 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25-29 October 2014. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, p. 1152-1158 How to Cite? |
Abstract | © 2014 Association for Computational Linguistics. Dependency parsing is a core task in NLP, and it is widely used by many applications such as information extraction, question answering, and machine translation. In the era of social media, a big challenge is that parsers trained on traditional newswire corpora typically suffer from the domain mismatch issue, and thus perform poorly on social media data. We present a new GFL/FUDG-annotated Chinese treebank with more than 18K tokens from Sina Weibo (the Chinese equivalent of Twitter). We formulate the dependency parsing problem as many small and parallelizable arc prediction tasks: for each task, we use a programmable probabilistic firstorder logic to infer the dependency arc of a token in the sentence. In experiments, we show that the proposed model outperforms an off-the-shelf Stanford Chinese parser, as well as a strong MaltParser baseline that is trained on the same in-domain data. |
Persistent Identifier | http://hdl.handle.net/10722/296125 |
DC Field | Value | Language |
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dc.contributor.author | Wang, William Yang | - |
dc.contributor.author | Kong, Lingpeng | - |
dc.contributor.author | Mazaitis, Kathryn | - |
dc.contributor.author | Cohen, William W. | - |
dc.date.accessioned | 2021-02-11T04:52:53Z | - |
dc.date.available | 2021-02-11T04:52:53Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25-29 October 2014. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, p. 1152-1158 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296125 | - |
dc.description.abstract | © 2014 Association for Computational Linguistics. Dependency parsing is a core task in NLP, and it is widely used by many applications such as information extraction, question answering, and machine translation. In the era of social media, a big challenge is that parsers trained on traditional newswire corpora typically suffer from the domain mismatch issue, and thus perform poorly on social media data. We present a new GFL/FUDG-annotated Chinese treebank with more than 18K tokens from Sina Weibo (the Chinese equivalent of Twitter). We formulate the dependency parsing problem as many small and parallelizable arc prediction tasks: for each task, we use a programmable probabilistic firstorder logic to infer the dependency arc of a token in the sentence. In experiments, we show that the proposed model outperforms an off-the-shelf Stanford Chinese parser, as well as a strong MaltParser baseline that is trained on the same in-domain data. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Dependency parsing for weibo: An efficient probabilistic logic programming approach | - |
dc.type | Conference_Paper | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3115/v1/d14-1122 | - |
dc.identifier.scopus | eid_2-s2.0-84961373726 | - |
dc.identifier.spage | 1152 | - |
dc.identifier.epage | 1158 | - |