2004 WordNetSimilarityMeasuringRelatedness

• (Pedersen et al., 2004) ⇒ Ted Pedersen, Siddharth Patwardhan, Jason Michelizzi. (2004). “WordNet::Similarity - Measuring the Relatedness of Concepts.” In: Proceedings of the Nineteenth National Conference on Artificial Intelligence - Intelligent Systems Demonstration (AAAI-04).

Subject Headings: Lexical Semantic Similarity Function, WordNet-based WSD Algorithm, WordNet-Similarity System.

Quotes

Abstract

• WordNet::Similarity is a freely available software package that makes it possible to measure the semantic similarity and relatedness between a pair of concepts (or synsets). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.

1 Introduction

• WordNet::Similarity implements measures of similarity and relatedness that are all in some way based on the structure and content of WordNet.
• Measures of similarity use information found in an is–a hierarchy of concepts (or synsets), and quantify how much concept A is like (or is similar to) concept B. For example, such a measure might show that an automobile is more like a boat than it is a tree, due to the fact that automobile and boat share vehicle as an ancestor in the WordNet noun hierarchy.
• WordNet is particularly well suited for similarity measures, since it organizes nouns and verbs into hierarchies of is–a relations. In version 2.0, there are nine separate noun hierarchies that include 80,000 concepts, and 554 verb hierarchies that are made up of 13,500 concepts. Is–a relations in WordNet do not cross part of speech boundaries, so similarity measures are limited to making judgments between noun pairs (e.g., cat and dog) and verb pairs (e.g., run and walk). While WordNet also includes adjectives and adverbs, these are not organized into is–a hierarchies so similarity measures can not be applied.
• However, concepts can be related in many ways beyond being similar to each other. For example, a wheel is a part of a car, night is the opposite of day, snow is made up of water, a knife is used to cut bread, and so forth. As such WordNet provides relations beyond is–a, including has–part, is–made–of, and is–an–attribute–of. In addition, each concept is defined by a short gloss that may include an example usage. All of this information can be brought to bear in creating measures of relatedness. As a result these measures tend to be more flexible, and allow for relatedness values to be assigned across parts of speech (e.g., the verb murder and the noun gun). This paper continues with an overview of the measures supported in WordNet::Similarity, and then provides a brief description of how the package can be used. We close with a summary of research that has employed WordNet::Similarity.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 WordNetSimilarityMeasuringRelatedness	Siddharth Patwardhan Ted Pedersen Jason Michelizzi			WordNet::Similarity - Measuring the Relatedness of Concepts			http://acl.ldc.upenn.edu/N/N04/N04-3012.pdf