Semantic measure based on features in lexical knowledge sources
Semantic measures between concepts require some of cognitive capabilities such as categorization and reasoning to estimate semantic association among concepts. For this reason, this problem has numerous applications in artificial intelligence, natural language processing, information retrieval, text...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2017
|
| Online Access: | http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/ http://journalarticle.ukm.my/11842/1/17781-54213-1-PB.pdf |
| Summary: | Semantic measures between concepts require some of cognitive capabilities such as categorization and reasoning to estimate semantic association among concepts. For this reason, this problem has numerous applications in artificial intelligence, natural language processing, information retrieval, text clustering, and text categorization. Measuring lexical semantic relatedness generally requires certain background information about the concept or terms. Semantic measures between concepts are divided into two main sources: knowledge based and unstructured corpora. Both resources play important role in the task of measuring lexical semantic relatedness. Knowledge-based semantic measures have been proposed to estimate semantic similarity between two concepts using several approaches such as ontology-based, graph-based and concept's vector approaches. This paper reviews existing semantic similarity measures which depend on the lexical source and discusses the various approaches on semantic measures which include the path-based, information content, gloss-based and feature-based measures. This paper also focuses on semantic measures that are based on features using lexical knowledge sources and discusses some issues that arise in these measures. |
|---|