Transcript Dimension of Meaning Author: Hinrich Schutze Presenter: Marian Olteanu

Dimension of Meaning
Author: Hinrich Schutze
Presenter: Marian Olteanu
Introduction
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Represent context as vectors
Dimensions of space – words
Initial vectors – determined by word
occurrence
This paper – reduce dimensionality by
singular value decomposition
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Applications
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WSD
Thesaurus induction
Introduction
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Classic scheme in IR
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Extension – represent contexts as vectors of
words within a fixed window
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Documents are represented as vectors of words
in term space
Disadvantage – content can be expressed with
different words, close in meaning
This approach
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Represent words as term vectors that reflect their
pattern of usage in a large corpus
Introduction
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Dimension in this
space:
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Cash
Sport
Measure
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Cosine of the angle
between vectors
Introduction
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Compute a representation of context more
robust than bag-of-words
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Centroid (normalized average) of the vectors of
the words in a context
Practical applications
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Thousands of dimensions (words)
Matrix of concurrence with only 10% zeros
Application
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WSD
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Done by clustering the
contexts
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AutoClass
Buckshot
Assign a sense for each
cluster
Word space
Window size, dimension sets
Discussion
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Resembles LSI
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Uses SVD
Purpose of space reduction
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LSI – improve the quality of representation
(because of null values)
This paper
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Reducing the computation
Detection of term dependencies (similar terms)
SVD doesn’t influence accuracy of WSD
Discussion
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Small number of parameters (thousands)
compared to other statistical approaches (i.e.:
trigrams)