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Academic Paper


Title: A non-negative tensor factorization model for selectional preference induction
Author: Tim Van De Cruys
Institution: Université Paris Diderot - Paris 7
Linguistic Field: Semantics; Text/Corpus Linguistics
Abstract: The distributional similarity methods have proven to be a valuable tool for the induction of semantic similarity. Until now, most algorithms use two-way co-occurrence data to compute the meaning of words. Co-occurrence frequencies, however, need not be pairwise. One can easily imagine situations where it is desirable to investigate co-occurrence frequencies of three modes and beyond. This paper will investigate tensor factorization methods to build a model of three-way co-occurrences. The approach is applied to the problem of selectional preference induction, and automatically evaluated in a pseudo-disambiguation task. The results show that tensor factorization, and non-negative tensor factorization in particular, is a promising tool for Natural Language Processing (nlp).

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 16, Issue 4, which you can read on Cambridge's site .



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