Effects of domain on measures of semantic relatedness

Abstract

Measures of semantic relatedness have been used in a variety of applications in information retrieval and language technology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance‐based calculations over WordNet or other taxonomies to statistical distributional metrics over document collections such as Wikipedia or the Web. 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.

Exploring Query Categorisation for Query Expansion: A Study

“…On the other hand, if the user explicitly indicates the domain of the query, this not only eliminates the query-classification step, but should also help to reduce any ambiguity that might be present. In recent work, Macias-Galindo et al [2015] have confirmed that the domain of interest is important when quantifying the semantically relatedness between words. Even though their experiments were not directly related to QE, their findings are expected to be applicable when estimating the semantic relation between the query and candidate expansion terms.…”