Distributional Measures as Proxies for Semantic Relatedness
This work addresses the need for more accurate semantic relatedness measures in natural language processing applications, but it is incremental as it builds on existing distributional and ontology-based approaches.
The paper tackled the problem of evaluating distributional measures for semantic relatedness by comparing them with human judgment and ontology-based methods, resulting in the identification of limitations and proposal of new measures to better align with human notions.
The automatic ranking of word pairs as per their semantic relatedness and ability to mimic human notions of semantic relatedness has widespread applications. Measures that rely on raw data (distributional measures) and those that use knowledge-rich ontologies both exist. Although extensive studies have been performed to compare ontological measures with human judgment, the distributional measures have primarily been evaluated by indirect means. This paper is a detailed study of some of the major distributional measures; it lists their respective merits and limitations. New measures that overcome these drawbacks, that are more in line with the human notions of semantic relatedness, are suggested. The paper concludes with an exhaustive comparison of the distributional and ontology-based measures. Along the way, significant research problems are identified. Work on these problems may lead to a better understanding of how semantic relatedness is to be measured.