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<Paper uid="E06-1013">
  <Title>Generalized Hebbian Algorithm for Incremental Singular Value Decomposition in Natural Language Processing</Title>
  <Section position="6" start_page="102" end_page="103" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> An incremental approach to approximating the singular value decomposition of a correlation matrix has been presented. Use  of the incremental approach means that singular value decomposition is an option in situations where data takes the form of single serially-presented observations from an unknown matrix. The method is particularly appropriate in natural language contexts, where datasets are often too large to be processed by traditional methods, and situations where the dataset is unbounded, for example in systems that learn through use. The approach produces preliminary estimations of the top vectors, meaning that information becomes available early in the training process. By avoiding matrix multiplication, data of high dimensionality can be processed. Results of preliminary experiments have been discussed here on the task of modelling word and letter bigrams. Future work will include an evaluation on much larger corpora.</Paragraph>
    <Paragraph position="1"> Acknowledgements: The author would like to thank Brandyn Webb for his contribution, and theGraduate Schoolof Language Technology and Vinnova for their financial support.</Paragraph>
  </Section>
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