Context Vectors: A Step Toward a "Grand Unified Representation"

Context Vectors are fixed-length vector representations useful for document retrieval and word sense disambiguation. Context vectors were motivated by four goals: 1 Capture “similarity of use” among words (“car” is similar to “auto”, but not similar to “hippopotamus”). 2 Quickly find constituent objects (eg., documents that contain specified words). 3 Generate context vectors automatically from an unlabeled corpus. 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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