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Dimension of Meaning - PowerPoint PPT Presentation

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

Author: Hinrich Schutze

Presenter: Marian Olteanu

• 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

• 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

• Dimension in this space:

• Cash

• Sport

• Measure

• Cosine of the angle between vectors

• 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

• WSD

• Done by clustering the contexts

• AutoClass

• Buckshot

• Assign a sense for each cluster

• 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

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