<?xml version="1.0" standalone="yes"?>
<Paper uid="N06-2020">
  <Title>Evaluation of Utility of LSA for Word Sense Discrimination</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The goal of the on-going project described in this paper is evaluation of the utility of Latent Semantic Analysis (LSA) for unsupervised word sense discrimination. The hypothesis is that LSA can be used to compute context vectors for ambiguous words that can be clustered together - with each cluster corresponding to a different sense of the word. In this paper we report first experimental result on tightness, separation and purity of sense-based clusters as a function of vector space dimensionality and using different distance metrics.</Paragraph>
  </Section>
class="xml-element"></Paper>