Applying maximum entropy to robust Chinese shallow parsing

Shih Hung Wu, Cheng Wei Shih, Chia Wei Wu, Tzong Han Tsai, Wen Lian Hsu

研究成果: 會議貢獻類型會議論文同行評審

8 引文 斯高帕斯(Scopus)

摘要

Recently, shallow parsing has been applied to various information processing systems, such as information retrieval, information extraction, question answering, and automatic document summarization. A shallow parser is suitable for online applications, because it is much more efficient and less demanding than a full parser. In this research, we formulate shallow parsing as a sequential tagging problem and use a supervised machine learning technique, Maximum Entropy (ME), to build a Chinese shallow parser. The major features of the ME-based shallow parser are POSs and the context words in a sentence. We adopt the shallow parsing results of Sinica Treebank as our standard, and select 30,000 and 10,000 sentences from Sinica Treebank as the training set and test set respectively. We then test the robustness of the shallow parser with noisy data. The experiment results show that the proposed shallow parser is quite robust for sentences with unknown proper nouns.

原文???core.languages.en_GB???
出版狀態已出版 - 2005
事件17th Conference on Computational Linguistics and Speech Processing, ROCLING 2005 - Tainan, Taiwan
持續時間: 15 9月 200516 9月 2005

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???event.eventtypes.event.conference???17th Conference on Computational Linguistics and Speech Processing, ROCLING 2005
國家/地區Taiwan
城市Tainan
期間15/09/0516/09/05

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