Lexical ontologies and semantic lexicons are important resources in natural language processing. They are used in various tasks and applications, especially where semantic processing is evolved such as question answering, machine translation, text understanding, information retrieval and extraction, content management, text summarization, knowledge acquisition and semantic search engines. Although there are a number of semantic lexicons for English and some other languages, Persian lacks such a complete resource to be used in NLP works. In this paper we introduce an ongoing project on developing a lexical ontology for Persian called FarsNet. We exploited a hybrid semi-automatic approach to acquire lexical and conceptual knowledge from resources such as WordNet, bilingual dictionaries, mono-lingual corpora and morpho-syntactic and semantic templates. FarsNet is an ontology whose elements are lexicalized in Persian. It provides links between various types of words (cross POS relations) and also between words and their corresponding concepts in other ontologies (cross ontologies relations). FarsNet aggregates the power of WordNet on nouns, the power of FrameNet on verbs and the wide range of conceptual relations from ontology community
In REF a semi automatic method has been used for developing a lexical ontology called FarsNet for Persian language.
Towards Semi Automatic Construction of a Lexical Ontology for Persian
LREC
Computer Science