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<Paper uid="C02-1025">
  <Title>Named Entity Recognition: A Maximum Entropy Approach Using Global Information</Title>
  <Section position="3" start_page="0" end_page="0" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> Recently, statistical NERs have achieved results that are comparable to hand-coded systems. Since MUC-6, BBN's Hidden Markov Model (HMM) based IdentiFinder (Bikel et al., 1997) has achieved remarkably good performance. MUC-7 has also seen hybrids of statistical NERs and hand-coded systems (Mikheev et al., 1998; Borthwick, 1999), notably Mikheev's system, which achieved the best performance of 93.39% on the official NE test data.</Paragraph>
    <Paragraph position="1"> MENE (Maximum Entropy Named Entity) (Borthwick, 1999) was combined with Proteus (a hand-coded system), and came in fourth among all MUC-7 participants. MENE without Proteus, however, did not do very well and only achieved an F-measure of 84.22% (Borthwick, 1999).</Paragraph>
    <Paragraph position="2"> Among machine learning-based NERs, IdentiFinder has proven to be the best on the official MUC-6 and MUC-7 test data. MENE (without the help of hand-coded systems) has been shown to be somewhat inferior in performance. By using the output of a hand-coded system such as Proteus, MENE can improve its performance, and can even outperform IdentiFinder (Borthwick, 1999).</Paragraph>
    <Paragraph position="3"> Mikheev et al. (1998) did make use of information from the whole document. However, their system is a hybrid of hand-coded rules and machine learning methods. Another attempt at using global information can be found in (Borthwick, 1999). He used an additional maximum entropy classifier that tries to correct mistakes by using reference resolution. Reference resolution involves finding words that co-refer to the same entity. In order to train this error-correction model, he divided his training corpus into 5 portions of 20% each. MENE is then trained on 80% of the training corpus, and tested on the remaining 20%. This process is repeated 5 times by rotating the data appropriately. Finally, the concatenated 5 * 20% output is used to train the reference resolution component. We will show that by giving the first model some global features, MENERGI outperforms Borthwick's reference resolution classifier. On MUC-6 data, MENERGI also achieves performance comparable to IdentiFinder when trained on similar amount of training data.</Paragraph>
    <Paragraph position="4"> In Section 5, we try to compare results of MENE, IdentiFinder, and MENERGI. However, both MENE and IdentiFinder used more training data than we did (we used only the official MUC-6 and MUC-7 training data). On the MUC-6 data, Bikel et al. (1997; 1999) do have some statistics that show how IdentiFinder performs when the training data is reduced. Our results show that MENERGI performs as well as IdentiFinder when trained on comparable amount of training data.</Paragraph>
  </Section>
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