Paper Info
Title
A graph-theoretic framework for semantic distance
Abstract
Many NLP applications entail that texts are classified based on their semantic distance (how similar or different the texts are). For example, comparing the text of a new document to that of documents of known topics can help identify the topic of the new text. Typically, a distributional distance is used to capture the implicit semantic distance between two pieces of text. However, such approaches do not take into account the semantic relations between words. In this article, we introduce an alternative method of measuring the semantic distance between texts that integrates distributional information and ontological knowledge within a network flow formalism. We first represent each text as a collection of frequency-weighted concepts within an ontology. We then make use of a network flow method which provides an efficient way of explicitly measuring the frequency-weighted ontological distance between the concepts across two texts. We evaluate our method in a variety of NLP tasks, and find that it performs well on two of three tasks. We develop a new measure of semantic coherence that enables us to account for the performance difference across the three data sets, shedding light on the properties of a data set that lends itself well to our method.
Year
DOI
Venue
2010
10.1162/coli.2010.36.1.36101
Computational Linguistics
Keywords
DocType
Volume
semantic coherence,network flow method,new text,alternative method,frequency-weighted ontological distance,implicit semantic distance,graph-theoretic framework,semantic distance,distributional distance,semantic relation,network flow
Journal
36
Issue
ISSN
Citations 
1
0891-2017
6
PageRank 
References 
Authors
0.46
40
2
Name
Order
Citations
PageRank
Vivian Tsang1494.21
Suzanne Stevenson256664.31