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<Paper uid="C90-3038">
  <Title>NEURAL NETWORK APPROACH TO WORD CATEGORY PREDICTION FOR ENGLISH TEXTS</Title>
  <Section position="4" start_page="217" end_page="217" type="concl">
    <SectionTitle>
6. Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we have presented the NETgram, a neural network for N-gram word category prediction in text. The NETgram can easily be expanded from Bigram to N-gram network without exponentially increasing the number of parameters.</Paragraph>
    <Paragraph position="1"> The training results showed that the Trigram word category prediction ability of the NETgram was comparable to that of the statistical Trigram model although the NgTgram requires fewer parameters than the statistical model. We also confirmed that NETgrams performed effectively for unknown data which never appeared in the training data, that is to say, NETgrams interpolate sparse training data naturally.</Paragraph>
  </Section>
class="xml-element"></Paper>