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Efficient Hybrid Semantic Text Similarity using Wordnet and a Corpus
| Content Provider | Semantic Scholar |
|---|---|
| Author | Atoum, Issa Otoom, Ahmed |
| Copyright Year | 2016 |
| Abstract | Text similarity plays an important role in natural language processing tasks such as answering questions and summarizing text. At present, state-of-the-art text similarity algorithms rely on inefficient word pairings and/or knowledge derived from large corpora such as Wikipedia. This article evaluates previous word similarity measures on benchmark datasets and then uses a hybrid word similarity in a novel text similarity measure (TSM). The proposed TSM is based on information content and WordNet semantic relations. TSM includes exact word match, the length of both sentences in a pair, and the maximum similarity between one word and the compared text. Compared with other well-known measures, results of TSM are surpassing or comparable with the best algorithms in the literature. |
| File Format | PDF HTM / HTML |
| DOI | 10.14569/IJACSA.2016.070917 |
| Volume Number | 7 |
| Alternate Webpage(s) | http://thesai.org/Downloads/Volume7No9/Paper_17-Efficient_Hybrid_Semantic_Text_Similarity_using_Wordnet.pdf |
| Alternate Webpage(s) | https://doi.org/10.14569/IJACSA.2016.070917 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
