Title :
Detecting hedges scope based on phrase structures and dependency structures
Author :
Zhou, Huiwei ; Li, Xiaoyan ; Huang, Degen ; Yang, Yuansheng ; Ma, Jianjun
Author_Institution :
Sch. of Comput. Sci. & Technol., Dalian Univ. of Technol., Dalian, China
Abstract :
To distinguish facts from unreliable or uncertain information, hedges have to be identified. This paper presents an approach to hedges scope detection based on phrase structures and dependency structures. First, phrase structures and dependency structures are used for hedges scope detection respectively. Phrase structures are adapted as important features for hedges scope detection by a machining learning method. Dependency structures are used to detect hedges scope by a rule-based method. Then, the phrase-based system and the dependency-based system are combined by a Conditional Random Field (CRF)-based model, which simply extends the feature vectors with the scope tags generated by the two individual phrase-based and dependency-based systems. Experiments on the CoNLL-2010 biological corpus show that our model achieves F-scores of 55.47% on hedges scope detection based on phrase structures using machine learning and 55.67% based on dependency structures using manual rules, and 58.97% based on dependency structures and phrase structures using our combined method. The analysis results show that phrase structures and dependency structures are both effective for hedges scope detection and their combination can improve the scope detection performance further.
Keywords :
bioinformatics; feature extraction; knowledge based systems; learning (artificial intelligence); natural language processing; random processes; text analysis; CoNLL-2010 biological corpus; biomedical text; conditional random field-based model; dependency structure; feature vectors; hedge scope detection; machine learning; phrase structure; phrase-based system; rule-based method; science text; scope tags; speculative language; uncertain information; unreliable information; Strontium; Conditional Random Field; dependency structures; manual rules; phrase structures;
Conference_Titel :
Natural Language Processing andKnowledge Engineering (NLP-KE), 2011 7th International Conference on
Conference_Location :
Tokushima
Print_ISBN :
978-1-61284-729-0
DOI :
10.1109/NLPKE.2011.6138235