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- W1796242380 abstract "This paper presents a Chinese entity detection and tracking system that takes advantages of character-based models and machine learning approaches. An entity here is defined as a link of all its mentions in text together with the associated attributes. Entity mentions of different types normally exhibit quite different linguistic patterns. Six separate Conditional Random Fields (CRF) models that incorporate character N-gram and word knowledge features are built to detect the extent and the head of three types of mentions, namely named, nominal and pronominal mentions. For each type of mentions, attributes are identified by Support Vector Machine (SVM) classifiers which take mention heads and their context as classification features. Mentions can then be merged into a unified entity representation by examining their attributes and connections in a rule-based coreference resolution process. The system is evaluated on ACE 2005 corpus and achieves competitive results." @default.
- W1796242380 created "2016-06-24" @default.
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- W1796242380 date "2007-01-01" @default.
- W1796242380 modified "2023-09-23" @default.
- W1796242380 title "Applying Machine Learning to Chinese Entity Detection and Tracking" @default.
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- W1796242380 doi "https://doi.org/10.1007/978-3-540-70939-8_14" @default.
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