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Effects of domain on measures of semantic relatedness
Author(s) -
MaciasGalindo Daniel,
Cavedon Lawrence,
Thangarajah John,
Wong Wilson
Publication year - 2015
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23303
Subject(s) - semantic similarity , similarity (geometry) , computer science , domain (mathematical analysis) , metric (unit) , set (abstract data type) , variety (cybernetics) , feature (linguistics) , natural language processing , information retrieval , cohesion (chemistry) , construct (python library) , artificial intelligence , mathematics , linguistics , mathematical analysis , operations management , philosophy , chemistry , organic chemistry , economics , image (mathematics) , programming language
Measures of semantic relatedness have been used in a variety of applications in i nformation r etrieval and l anguage t echnology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance‐based calculations over W ord N et or other taxonomies to statistical distributional metrics over document collections such as W ikipedia or the W eb. Existing measures do not explicitly consider the domain associations of terms when calculating relatedness: This article demonstrates that domain matters. We construct a data set of pairs of terms with associated domain information and extract pairs that are scored nearly identical by a sample of existing semantic‐relatedness measures. We show that human judgments reliably score those pairs containing terms from the same domain as significantly more related than cross‐domain pairs, even though the semantic‐relatedness measures assign the pairs similar scores. We provide further evidence for this result using a machine learning setting by demonstrating that domain is an informative feature when learning a metric. We conclude that existing relatedness measures do not account for domain in the same way or to the same extent as do human judges.