Dimension of meaning
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Dimension of Meaning. Author: Hinrich Schutze Presenter: Marian Olteanu. Introduction. Represent context as vectors Dimensions of space – words Initial vectors – determined by word occurrence This paper – reduce dimensionality by singular value decomposition Applications WSD

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Dimension of meaning l.jpg

Dimension of Meaning

Author: Hinrich Schutze

Presenter: Marian Olteanu


Introduction l.jpg
Introduction

  • Represent context as vectors

  • Dimensions of space – words

  • Initial vectors – determined by word occurrence

  • This paper – reduce dimensionality by singular value decomposition

    • Applications

      • WSD

      • Thesaurus induction


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Introduction

  • Classic scheme in IR

    • Documents are represented as vectors of words in term space

  • Extension – represent contexts as vectors of words within a fixed window

    • Disadvantage – content can be expressed with different words, close in meaning

  • This approach

    • Represent words as term vectors that reflect their pattern of usage in a large corpus


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Introduction

  • Dimension in this space:

    • Cash

    • Sport

  • Measure

    • Cosine of the angle between vectors


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Introduction

  • Compute a representation of context more robust than bag-of-words

    • Centroid (normalized average) of the vectors of the words in a context

  • Practical applications

    • Thousands of dimensions (words)

    • Matrix of concurrence with only 10% zeros


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Application

  • WSD

    • Done by clustering the contexts

      • AutoClass

      • Buckshot

    • Assign a sense for each cluster




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Discussion

  • Resembles LSI

    • Uses SVD

  • Purpose of space reduction

    • LSI – improve the quality of representation (because of null values)

    • This paper

      • Reducing the computation

      • Detection of term dependencies (similar terms)

    • SVD doesn’t influence accuracy of WSD


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Discussion

  • Small number of parameters (thousands) compared to other statistical approaches (i.e.: trigrams)


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