Named Entity Recognition using an HMM-based Chunk Tagger

Named Entity Recognition using an HMM-based Chunk Tagger GuoDong Zhou Jian Su Laboratories for Information Technology 21 Heng Mui Keng Terrace Singapore 119613 zhougd@lit.org.sg sujian@lit.org.sg Abstract This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules. 1 Introduction Named Entity (NE) Recognition (NER) is to classify every word in a document into some predefined categories and "none-of-the-above". In the taxonomy of computational linguistics tasks, it falls under the domain of "information extraction", which extracts specific kinds of information from documents as opposed to the more general task of "document management" which seeks to extract all of the information found in a document. Since entity names form the main content of a document, NER is a very important step toward more intelligent information extraction and management. The atomic elements of information extraction -- indeed, of language as a whole -- could be considered as the "who", "where" and "how much" in a sentence. NER performs what is known as surface parsing, delimiting sequences of tokens that answer these important questions. NER can also be used as the first step in a chain of processors: a next level of processing could relate two or more NEs, or perhaps even give semantics to that relationship using a verb. In this way, further processing could discover the "what" and "how" of a sentence or body of text. While NER is relatively simple and it is fairly easy to build a system with reasonable performance, there are still a large number of ambiguous cases that make it difficult to attain human performance. There has been a considerable amount of work on NER problem, which aims to address many of these ambiguity, robustness and portability issues. During last decade, NER has drawn more and more attention from the NE tasks [Chinchor95a] [Chinchor98a] in MUCs [MUC6] [MUC7], where person names, location names, organization names, dates, times, percentages and money amounts are to be delimited in text using SGML mark-ups. Previous approaches have typically used manually constructed finite state patterns, which attempt to match against a sequence of words in much the same way as a general regular expression matcher. Typical systems are Univ. of Sheffield's LaSIE-II [Humphreys+98], ISOQuest's NetOwl [Aone+98] [Krupha+98] and Univ. of Edinburgh's LTG [Mikheev+98] [Mikheev+99] for English NER. …

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