Teaching Machines to Find Names

Introduction

In the field of Natural Language Processing, one of the very important research areas of Information Extraction (IE) comes in Named Entity Recognition (NER). NER is a subtask of IE that seeks to identify and classify the predefined categories of named entities in text documents. Considerable amount of work has been done on NER in recent years due to the increasing demand of automated texts and the wide availability of electronic corpora. While it is relatively easy and natural for a human reader to read and understand the context of a given article, getting a machine to understand and differentiate between words is a big challenge. For instance, the word ‘brown’ may refer to a person called Mr. Brown, or the colour of an item which is brown. Human readers can easily discern the meaning of the word by looking at the context of that particular sentence, but it would be almost impossible for a computer to interpret it without any additional information.

To deal with the issue, researchers in NER field have proposed various rule-based systems (Wakao, Gaizauskas & Wilks, 1996; Krupka & Hausman, 1998; Maynard, Tablan, Ursu, Cunningham & Wilks, 2001). These systems are able to achieve high accuracy in recognition with the help of some lists of known named entities called gazetteers. The problem with rule-based approach is that it lacks the robustness and portability. It incurs steep maintenance cost especially when new rules need to be introduced for some new information or new domains.

A better option is thus to use machine learning approach that is trainable and adaptable. Three well-known machine learning approaches that have been used extensively in NER are Hidden Markov Model (HMM), Maximum Entropy Model (MEM) and Decision Tree. Many of the existing machine learning-based NER systems (Bikel, Schwartz & Weischedel, 1999; Zhou & Su, 2002; Borthwick, Sterling, Agichten & Grisham, 1998; Bender, Och & Ney, 2003; Chieu & Ng, 2002; Sekine, Grisham & Shinnou, 1998) are able to achieve near-human performance for named entity tagging, even though the overall performance is still about 2% short from the rule-based systems.

There have also been many attempts to improve the performance of NER using a hybrid approach with the combination of handcrafted rules and statistical models (Mikheev, Moens & Grover, 1999; Srihari & Li, 2000; Seon, Ko, Kim & Seo, 2001). These systems can achieve relatively good performance in the targeted domains owing to the comprehensive handcrafted rules. Nevertheless, the portability problem still remains unsolved when it comes to dealing with NER in various domains.

As such, this article presents a hybrid machine learning approach using MEM and HMM successively. The reason for using two statistical models in succession instead of one is due to the distinctive nature of the two models. HMM is able to achieve better performance than any other statistical models, and is generally regarded as the most successful one in machine learning approach. However, it suffers from sparseness problem, which means considerable amount of data is needed for it to achieve acceptable performance. On the other hand, MEM is able to maintain reasonable performance even when there is little data available for training purpose. The idea is therefore to walkthrough the testing corpus using MEM first in order to generate a temporary tagging result, while this procedure can be simultaneously used as a training process for HMM. During the second walkthrough, the corpus uses HMM for the final tagging. In this process, the temporary tagging result generated by MEM will be used as a reference for subsequent error checking and correction. In the case when there is little training data available, the final result can still be reliable based on the contribution of the initial MEM tagging result.