A Hybrid Approach for Named Entity and Sub-Type Tagging

A Hybrid Approach for Named Entity and Sub-Type Tagging* Rohini Srihari Cymfony Net, Inc. 5500 Main Street Williamsville, NY 14260 rohini @ cymfony.com Cheng Niu and Wei Li Cymfony Net, Inc. 5500 Main Street Williamsville, NY 14260 chengniu@cymfony.com wei@cymfony.com Abstract This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Each has innate strengths and weaknesses; the combination results in a very high precision tagger. MaxEnt includes external gazetteers in the system. Sub-category generation is also discussed. Introduction Named entity (NE) tagging is a task in which location names, person names, organization names, monetary amounts, time and percentage expressions are recognized and classified in unformatted text documents. This task provides important semantic information, and is a critical first step in any information extraction system. Intense research has been focused on improving NE tagging accuracy using several different techniques. These include rule-based systems [Krupka 1998], Hidden Markov Models (HMM) [Bikel et al. 1997] and Maximum Entropy Models (MaxEnt) [Borthwick 1998]. A system based on manual rules may provide the best performance; however these require painstaking intense skilled labor.. Furthermore, shifting domains involves significant effort and may result in performance degradation. The strength of HMM models lie in their capacity for modeling local contextual information. HMMs have been widely used in continuous speech recognition, part-of-speech tagging, OCR, etc., and are generally regarded as the most successful statistical modelling paradigm in these domains. MaxEnt is a powerful tool to be used in situations where several ambiguous information sources need to be combined. Since statistical techniques such as HMM are only as good as the data they are trained on, they are required to use back-off models to compensate for unreliable statistics. In contrast to empirical back-off models used in HMMs, MaxEnt provides a systematic method by which a statistical model consistent with all obtained knowledge can be trained. [Borthwick et al. 1998] discuss a technique for combining the output of several NE taggers in a black box fashion by using MaxEnt. They demonstrate the superior performance of this system; however, the system is computationally inefficient since many taggers need to be run. In this paper we propose a hybrid method for NE tagging which combines all the modelling techniques mentioned above. NE tagging is a complex task and high-performance systems are required in order to be practically usable. Furthermore, the task demonstrates characteristics that can be exploited by all three techniques. For example, time and monetary expressions are fairly predictable and hence processed most efficiently with handcrafted grammar rules. Name, location and organization entities are highly variable and thus lend themselves to statistical training algorithms such as HMMs. Finally, many conflicting pieces of information regarding the class of a tag are * This work was supported in part by the SBIR grant F30602-98-C-0043 from Air Force Research Laboratory (AFRL)/IFED. 247 frequently present. This includes information from less than perfect gazetteers. For this, a MaxEnt approach works well in …

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