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<Paper uid="C90-3038">
  <Title>NEURAL NETWORK APPROACH TO WORD CATEGORY PREDICTION FOR ENGLISH TEXTS</Title>
  <Section position="1" start_page="0" end_page="0" type="metho">
    <SectionTitle>
NEURAL NETWORK APPROACH TO WORD CATEGORY PREDICTION
FOR ENGLISH TEXTS
Masami NAKAMURA, Katsuteru MARUYAMA f, Takeshi KAWABATA fC/, Kiyohiro SHIKANO tit
ATR Interpreting Telephony Research Laboratories
</SectionTitle>
    <Paragraph position="0"/>
  </Section>
  <Section position="2" start_page="0" end_page="0" type="metho">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Word category prediction is used to implement an accurate word recognition system. Traditional statistical approaches require considerable training data to estimate the probabilities of word sequences, and many parameters to memorize probabilities. To solve this problem, NETgram, which is the neural network for word category prediction, is proposed. Training results show that the perfornmnce of tim NETgram is comparable to that of the statistical model ;although the NETgram requires fewer parameters than the ~;tatisticat model. Also the NETgram performs effectively PSor unknown data, i.e., the NETgram interpolates sparse training data. Results of analyzing the hidden layer show that the word categories are classified into linguistically ti~ignificant groups. The results of applying the NETgram to HMM English word recognition show that the NETgram improves the word recognition rate fi'om 81.0% to 86.9%.</Paragraph>
  </Section>
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