test Browse by Author Names Browse by Titles of Works Browse by Subjects of Works Browse by Issue Dates of Works

Advanced Search
& Collections
Issue Date   
Sign on to:   
Receive email
My Account
authorized users
Edit Profile   
About T-Space   

T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Doctoral >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/11238

Title: Measuring Semantic Distance using Distributional Profiles of Concepts
Authors: Mohammad, Saif
Advisor: Hirst, Graeme
Department: Computer Science
Keywords: Computational Linguistics
Natural Language Processing
Lexical semantics
semantic distance
distributional similarity
semantic similarity
semantic relatedness
word concept co-occurrence matrix
distributional profiles of concepts
corpus-based techniques
word senses
cross-lingual techniques
word sense dominance
word sense disambiguation
Issue Date: 1-Aug-2008
Abstract: Semantic distance is a measure of how close or distant in meaning two units of language are. A large number of important natural language problems, including machine translation and word sense disambiguation, can be viewed as semantic distance problems. The two dominant approaches to estimating semantic distance are the WordNet-based semantic measures and the corpus-based distributional measures. In this thesis, I compare them, both qualitatively and quantitatively, and identify the limitations of each. This thesis argues that estimating semantic distance is essentially a property of concepts (rather than words) and that two concepts are semantically close if they occur in similar contexts. Instead of identifying the co-occurrence (distributional) profiles of words (distributional hypothesis), I argue that distributional profiles of concepts (DPCs) can be used to infer the semantic properties of concepts and indeed to estimate semantic distance more accurately. I propose a new hybrid approach to calculating semantic distance that combines corpus statistics and a published thesaurus (Macquarie Thesaurus). The algorithm determines estimates of the DPCs using the categories in the thesaurus as very coarse concepts and, notably, without requiring any sense-annotated data. Even though the use of only about 1000 concepts to represent the vocabulary of a language seems drastic, I show that the method achieves results better than the state-of-the-art in a number of natural language tasks. I show how cross-lingual DPCs can be created by combining text in one language with a thesaurus from another. Using these cross-lingual DPCs, we can solve problems in one, possibly resource-poor, language using a knowledge source from another, possibly resource-rich, language. I show that the approach is also useful in tasks that inherently involve two or more languages, such as machine translation and multilingual text summarization. The proposed approach is computationally inexpensive, it can estimate both semantic relatedness and semantic similarity, and it can be applied to all parts of speech. Extensive experiments on ranking word pairs as per semantic distance, real-word spelling correction, solving Reader's Digest word choice problems, determining word sense dominance, word sense disambiguation, and word translation show that the new approach is markedly superior to previous ones.
URI: http://hdl.handle.net/1807/11238
Appears in Collections:Doctoral
Department of Computer Science - Doctoral theses

Files in This Item:

File Description SizeFormat
Mohammad_Saif_M_200806_PhD_thesis.pdf1.23 MBAdobe PDF

This item is licensed under a Creative Commons License
Creative Commons

Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.