Turk Bootstrap Word Sense Inventory 2.0: A Large-Scale Resource for Lexical Substitution

13901

Authors

nan

Abstract

AbstractThis paper presents the Turk Bootstrap Word Sense Inventory (TWSI) 2.0. This lexical resource, created by a crowdsourcing process using Amazon Mechanical Turk (http://www.mturk.com), encompasses a sense inventory for lexical substitution for 1,012 highly frequent English common nouns. Along with each sense, a large number of sense-annotated occurrences in context are given, as well as a weighted list of substitutions. Sense distinctions are not motivated by lexicographic considerations, but driven by substitutability: two usages belong to the same sense if their substitutions overlap considerably. After laying out the need for such a resource, the data is characterized in terms of organization and quantity. Then, we briefly describe how this data was used to create a system for lexical substitutions. Training a supervised lexical substitution system on a smaller version of the resource resulted in well over 90% acceptability for lexical substitutions provided by the system. Thus, this resource can be used to set up reliable, enabling technologies for semantic natural language processing (NLP), some of which we discuss briefly.

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Labeled Text

AbstractThis paper presents the Turk Bootstrap Word Sense Inventory (TWSI) 2.0. This lexical resource, created by a crowdsourcing process using Amazon Mechanical Turk ( encompasses a sense inventory for lexical substitution for 1,012 highly frequent English common nouns. Along with each sense, a large number of sense-annotated occurrences in context are given, as well as a weighted list of substitutions. Sense distinctions are not motivated by lexicographic considerations, but driven by substitutability: two usages belong to the same sense if their substitutions overlap considerably. After laying out the need for such a resource, the data is characterized in terms of organization and quantity. Then, we briefly describe how this data was used to create a system for lexical substitutions. Training a supervised lexical substitution system on a smaller version of the resource resulted in well over 90% acceptability for lexical substitutions provided by the system. Thus, this resource can be used to set up reliable, enabling technologies for semantic natural language processing (NLP), some of which we discuss briefly.

Publication Details

Paper ID:
13901
Year:
2012
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan