Constructing Dictionaries for Named Entity Recognition on Specific Domains from the Web

This paper describes an automatic dictionary construction method for Named Entity Recognition (NER) on specific domains such as restaurant guides. NER is the first step toward Information Extraction (IE), and we believe that such a dictionary construction method for NER is crucial for developing IE systems for a wide range of domains in the World Wide Web (WWW). One serious problem in NER on specific domains is that the performance of NER heavily depends on the amount of the training corpus, which requires much human labor to develop. We attempt to improve the performance of NER by using dictionaries automatically constructed from HTML documents instead of by preparing a large annotated corpus. Our dictionary construction method exploits the cooccurrence strength of two expressions in HTML itemizations calculated from average mutual information. Experimental results show that the constructed dictionaries improved the performance of the NER on a restaurant guide domain. Our method increased the F1-measure by 2.3 without any additional manual labor.