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The Importance Of Word-Net-Use Clustering Performance

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WordNet In (Bouras and Tsogkas, 2012), the importance of WordNet hypernymy relationships is highlighted in enhancing K-means clustering algorithm. Similar to the procedure prior to clustering process, an aggregate hypernym graph is generated to label a resulting cluster. The effect of other relationships, on the clustering performance, is not studied. Another Word-Net-based clustering method is presented in (Fodeh et al., 2011), where the role of nouns, especially polysemous and synonymous nouns in document clustering is investigated. A subset of core semantic features is chosen from disambiguated nouns through an unsupervised information gain measure. These core semantic features lead to admissible clustering results. The effect of…show more content…
(Motazedi et al., 2009) and (Lesk, 1986) introduce a bilingual translation machine called PEnTrans. A novel WSD method is proposed based on Lesk algorithm (Sarrafzadeh et al., 2011). In order to English to Persian translation, gloss, synset and ancestors in the radius of two hypernyms are extracted from WordNet, for each word’s sense. Also the POS and WSD tags are included (extracted from extended WordNet). The authors developed a bilingual dictionary by translation WordNet senses into Persian. For Persian to English translation a combination of knowledge, rule and corpus based approaches are utilized and also grammatical roles of words are considered in the WSD. 5. SEMANTIC ANALYSIS USING FARSNET 5.1 FarsNet Lexical Ontology The ontology is an abstract model of real world that demonstrates the concepts and the relations among them in a specific domain. This conceptual knowledge base has vital applications in semantic web, search engines, natural language processing, information retrieval, etc. The ontologies can be produced manually or in a semi-automatic manner by the ontology engineering tools and knowledge acquisition methods (Darrudi et al., 2004). FarsNet is the first Persian WordNet (Shamsfard et al., 2010) which has been produced in NLP laboratory of Shahid Beheshti University, Iran. The first version of FarsNet includes 18000 Persian words organized in about 10000 synsets. The words are in three syntactic
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