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Guy Lebanon
Purdue University - Machine Learning Reading Group
Sequential Document Representations and Simplicial Curves
May 19, 2006 12:00pm
Abstract:
The popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any sequential information. We present a continuous and differentiable sequential document representation that goes beyond the bag of words assumption, and yet is efficient and effective. This representation employs smooth curves in the multinomial simplex to account for sequential information. In contrast to n-grams the new representation is able to robustly model long-rage sequential trends in the paper. We discuss the representation and its geometric properties and demonstrate its applicability for the task of text classification.
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