scholarly journals Distributional Similarity in the Varied Order of Syntactic Spaces

2006 ◽  
Author(s):  
Dongqiang Yang ◽  
D.M.W. Powers
Keyword(s):  
Distributional Similarity

A semi-supervised approach for dimensionality reduction with distributional similarity

2013 ◽  
Vol 103 ◽  
pp. 210-221 ◽  
Author(s):  
Feng Zheng ◽  
Zhan Song ◽  
Ling Shao ◽  
Ronald Chung ◽  
Kui Jia ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Distributional Similarity

A non-negative tensor factorization model for selectional preference induction

2010 ◽  
Vol 16 (4) ◽  
pp. 417-437 ◽  
Author(s):  
TIM VAN DE CRUYS
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Tensor Factorization ◽  
Promising Tool ◽  
Occurrence Data ◽  
Factorization Model ◽  
Distributional Similarity ◽  
Factorization Methods

AbstractThe 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).

scholarly journals Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity

2005 ◽  
Vol 31 (4) ◽  
pp. 439-475 ◽  
Author(s):  
Julie Weeds ◽  
David Weir
Keyword(s):  
Language Processing ◽  
Similarity Measures ◽  
Document Retrieval ◽  
Wide Range ◽  
Distributional Similarity ◽  
Potential Applications ◽  
The Difference ◽  
Occurrence Type ◽  
Definition Of ◽  
Relationship Of

Techniques that exploit knowledge of distributional similarity between words have been proposed in many areas of Natural Language Processing. For example, in language modeling, the sparse data problem can be alleviated by estimating the probabilities of unseen co-occurrences of events from the probabilities of seen co-occurrences of similar events. In other applications, distributional similarity is taken to be an approximation to semantic similarity. However, due to the wide range of potential applications and the lack of a strict definition of the concept of distributional similarity, many methods of calculating distributional similarity have been proposed or adopted. In this work, a flexible, parameterized framework for calculating distributional similarity is proposed. Within this framework, the problem of finding distributionally similar words is cast as one of co-occurrence retrieval (CR) for which precision and recall can be measured by analogy with the way they are measured in document retrieval. As will be shown, a number of popular existing measures of distributional similarity are simulated with parameter settings within the CR framework. In this article, the CR framework is then used to systematically investigate three fundamental questions concerning distributional similarity. First, is the relationship of lexical similarity necessarily symmetric, or are there advantages to be gained from considering it as an asymmetric relationship? Second, are some co-occurrences inherently more salient than others in the calculation of distributional similarity? Third, is it necessary to consider the difference in the extent to which each word occurs in each co-occurrence type? Two application-based tasks are used for evaluation: automatic thesaurus generation and pseudo-disambiguation. It is possible to achieve significantly better results on both these tasks by varying the parameters within the CR framework rather than using other existing distributional similarity measures; it will also be shown that any single unparameterized measure is unlikely to be able to do better on both tasks. This is due to an inherent asymmetry in lexical substitutability and therefore also in lexical distributional similarity.

scholarly journals Characterising measures of lexical distributional similarity

2004 ◽  
Author(s):  
Julie Weeds ◽  
David Weir ◽  
Diana McCarthy
Keyword(s):  
Distributional Similarity

scholarly journals Matching the Blanks: Distributional Similarity for Relation Learning

2019 ◽  
Author(s):  
Livio Baldini Soares ◽  
Nicholas FitzGerald ◽  
Jeffrey Ling ◽  
Tom Kwiatkowski
Keyword(s):  
Distributional Similarity ◽  
Relation Learning
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