Computer Science – Computation and Language
Scientific paper
2012-03-08
Computer Science
Computation and Language
Scientific paper
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.
Hirst Graeme
Mohammad Saif M.
No associations
LandOfFree
Distributional Measures as Proxies for Semantic Relatedness does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Distributional Measures as Proxies for Semantic Relatedness, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distributional Measures as Proxies for Semantic Relatedness will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-18045