Abstract
Context Vectors are fixed-length vector representations useful for document retrieval and word sense disambiguation. Context vectors were motivated by four goals:
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1
Capture “similarity of use” among words (“car” is similar to “auto”, but not similar to “hippopotamus”).
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2
Quickly find constituent objects (eg., documents that contain specified words).
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3
Generate context vectors automatically from an unlabeled corpus.
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4
Use context vectors as input to standard learning algorithms.
Context Vectors lack, however, a natural way to represent syntax, discourse, or logic. Accommodating all these capabilities into a “Grand Unified Representation” is, we maintain, a prerequisite for solving the most difficult problems in Artificial Intelligence, including natural language understanding.
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Gallant, S.I. (2000). Context Vectors: A Step Toward a “Grand Unified Representation”. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_14
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DOI: https://doi.org/10.1007/10719871_14
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