Named Entity Recognition: A Maximum Entropy Approach Using Global Information

Named Entity Recognition: A Maximum Entropy Approach Using Global Information Hai Leong Chieu DSO National Laboratories 20 Science Park Drive Singapore 118230 chaileon@dso.org.sg Hwee Tou Ng Department of Computer Science School of Computing National University of Singapore 3 Science Drive 2 Singapore 117543 nght@comp.nus.edu.sg Abstract This paper presents a maximum entropy-based named entity recognizer (NER). It differs from pre- vious machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentence- based classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves perfor- mance that is comparable to the best previous ma- chine learning-based NERs on MUC-6 and MUC-7 test data. 1 Introduction Considerable amount of work has been done in re- cent years on the named entity recognition task, partly due to the Message Understanding Confer- ences (MUC). A named entity recognizer (NER) is useful in many NLP applications such as informa- tion extraction, question answering, etc. On its own, a NER can also provide users who are looking for person or organization names with quick informa- tion. In MUC-6 and MUC-7, the named entity task is defined as finding the following classes of names: person, organization, location, date, time, money, and percent (Chinchor, 1998; Sundheim, 1995) Machine learning systems in MUC-6 and MUC- 7 achieved accuracy comparable to rule-based sys- tems on the named entity task. Statistical NERs usually find the sequence of tags that maximizes the probability ����� � , where � is the sequence of words in a sentence, and � is the sequence of named-entity tags assigned to the words in � . Attempts have been made to use global information (e.g., the same named entity oc- curring in different sentences of the same docu- ment), but they usually consist of incorporating an additional classifier, which tries to correct the er- rors in the output of a first NER (Mikheev et al., 1998; Borthwick, 1999). We propose maximizing �����  � , where � is the sequence of named- entity tags assigned to the words in the sentence � , and  is the information that can be extracted from the whole document containing � . Our sys- tem is built on a maximum entropy classifier. By making use of global context, it has achieved ex- cellent results on both MUC-6 and MUC-7 official test data. We will refer to our system as MENERGI (Maximum Entropy Named Entity Recognizer us- ing Global Information). As far as we know, no other NERs have used in- formation from the whole document (global) as well as information within the same sentence (local) in one framework. The use of global features has im- proved the performance on MUC-6 test data from 90.75% to 93.27% (27% reduction in errors), and the performance on MUC-7 …

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