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<Paper uid="W06-2501">
  <Title>Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts</Title>
  <Section position="9" start_page="6" end_page="6" type="relat">
    <SectionTitle>
8 Related Work
</SectionTitle>
    <Paragraph position="0"> (Wilks et al., 1990) describe a word sense disambiguation algorithm that also uses vectors to determine the intended sense of an ambiguous word.</Paragraph>
    <Paragraph position="1"> In their approach, they use dictionary definitions from LDOCE (Procter, 1978). The words in these definitions are used to build a co-occurrence matrix, which is very similar to our technique of using the WordNet glosses for our Word Space.</Paragraph>
    <Paragraph position="2"> They augment their dictionary definitions with similar words, which are determined using the co-occurrence matrix. Each concept in LDOCE is then represented by an aggregate vector created by adding the co-occurrence counts for each of the words in the augmented definition of the concept.</Paragraph>
    <Paragraph position="3"> The next step in their algorithm is to form a context vector. The context of the ambiguous word is first augmented using the co-occurrence matrix, just like the definitions. The context vector is formed by taking the aggregate of the word vectors of the words in the augmented context. To disambiguate the target word, the context vector is compared to the vectors corresponding to each meaning of the target word in LDOCE, and that meaning is selected whose vector is mathematically closest to that of the context.</Paragraph>
    <Paragraph position="4"> Our approach differs from theirs in two primary respects. First, rather than creating an aggregate vector for the context we compare the vector of each meaning of the ambiguous word with the vectors of each of the meanings of the words in the context. This adds another level of indirection in the comparison and attempts to use only the relevant meanings of the context words. Secondly, we use the structure of WordNet to augment the short glosses with other related glosses.</Paragraph>
    <Paragraph position="5"> (Niwa and Nitta, 1994) compare dictionary based vectors with co-occurrence based vectors, where the vector of a word is the probability that an origin word occurs in the context of the word.</Paragraph>
    <Paragraph position="6"> These two representations are evaluated by applying them to real world applications and quantifying the results. Both measures are first applied to word sense disambiguation and then to the learning of positives or negatives, where it is required to determine whether a word has a positive or negative connotation. It was observed that the co-occurrence based idea works better for the word sense disambiguation and the dictionary based approach gives better results for the learning of positives or negatives. From this, the conclusion is that the dictionary based vectors contain some different semantic information about the words and warrants further investigation. It is also observed that for the dictionary based vectors, the network of words is almost independent of the dictionary that is used, i.e. any dictionary should give us almost the same network.</Paragraph>
    <Paragraph position="7"> (Inkpen and Hirst, 2003) also use gloss-based context vectors in their work on the disambiguation of near-synonyms - words whose senses are almost indistinguishable. They disambiguate near-synonyms in text using various indicators, one of which is context-vector-based. Context Vectors are created for the context of the target word and also for the glosses of each sense of the target word. Each gloss is considered as a bag of words, where each word has a corresponding Word Vector. These vectors for the words in a gloss are averaged to get a Context Vector corresponding to the gloss. The distance between the vector corresponding to the text and that corresponding to the gloss is measured (as the cosine of the angle between the vectors). The nearness of the vectors is used as an indicator to pick the correct sense of the target word.</Paragraph>
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