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Titlebook: Natural Language Processing – IJCNLP 2005; Second International Robert Dale,Kam-Fai Wong,Oi Yee Kwong Conference proceedings 2005 Springer-

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Confirmed Knowledge Acquisition Using Mails Posted to a Mailing Listcuments. It is almost inevitable that natural language documents, especially web documents, contain wrong information. As a result, it is important to investigate a method of detecting and correcting wrong information in natural language documents when we develop a knowledge base by using them. In t
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PP-Attachment Disambiguation Boosted by a Gigantic Volume of Unambiguous Exampless are utilized to acquire precise lexical preferences for PP-attachment disambiguation. Attachment decisions are made by a machine learning method that optimizes the use of the lexical preferences. Our experiments indicate that the precise lexical preferences work effectively.
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Adapting a Probabilistic Disambiguation Model of an HPSG Parser to a New Domain model of the original HPSG parser, we develop a log-linear model with additional features on a treebank of the biomedical domain. Since the treebank of the target domain is limited, we need to exploit an original disambiguation model that was trained on a larger treebank. Our model incorporates the
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A Hybrid Approach to Single and Multiple PP Attachment Using WordNetre. In this paper, we propose an algorithm to disambiguate between PP attachment sites. The algorithm uses a combination of supervised and unsupervised learning along with the WordNet information, which is implemented using a back-off model. Our use of the available sources of lexical knowledge base
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