Extraction of Verbal Synsets and Relations for F ars N et (2018)

Abstract: WordNet or ontology development for resource-poor languages like Persian, requires composition of several strategies and employment of appropriate heuristics. Lexical and linguistic structured resources are limited for Persian and there is a lot of diversity and structural and syntagmatic complexities. This paper proposes a system for extraction of verbal synsets and relations to extend FarsNet (Persian WordNet). The proposed method extracts verbal words and concepts using noun and adjective words and synsets. It exploits the data from digital lexicon glossaries, which leads to the identification of 6890 proper verbal words and 2790 verbal synsets, with 91% and 67% precision respectively. The proposed system also extracts relations such as semantic roles of verbal arguments (instrument, location, agent, and patient) and also “related-to” (unlabeled) relations and co-occurrence among verbs and other concepts. For this purpose, a combination of linguistic approaches such as morphological analysis of words, semantic analysis, and use of key phrases and syntactic and semantic patterns, corpus-based approach, statistical techniques and co-occurrence analysis have been utilized. The presented strategy extracts 5600 proper relations between the existing concepts in FarsNet 2.0 with 76% precision.

## introduction semantic or conceptual relation extraction between concepts and appropriate relation labeling forms an important part of ontology learning and ontology construction process that is widely used in information retrieval, question-answering systems, summarization, and word sense disambiguation (wsd) @xcite . learning and labeling of conceptual relations has been introduced as the most complex and challenging element in most of systems, especially in the construction of ontologies or @xcite . this problem can be divided into two separate parts of relationship extraction and labeling. the latter which tries to label an existing unlabeled relation between two concepts has been less addressed in previous studies. semantic analysis requires composition of various approaches like pattern-based and corpus-based techniques for languages such as persian that lack accurate analytical instruments and structured sources and suitable tagged corpora. thus, several lexical resources including syntactic verbal valency lexicon @xcite , comprehensive lexicon of synonyms and antonyms @xcite , online and digital lexicons such as vajehyab browser foot_0 , wikipedia, dadegan dependency treebank @xcite and wortschatz statistical corpus @xcite have been used in the presented strategy. in addition, persian preprocessing tools such as negar text editing tool, step-1 morphological analyzer @xcite and parsipardaz dependency parser @xcite , and a composition of linguistic, syntactic, and statistical approaches have been used for semantic relation extraction. as verbs are the main core of sentences in many languages, extending the verbal part of wordnets may improve their efficiency and application in semantic analysis of texts. this paper focuses on extraction of verbs, verbal synsets and non-taxonomic relations in which at least one of the related terms is verb. the given strategy in this paper for extraction of verbal concepts emphasizes on wide range of compound verbs and prefixes in persian with highly metaphorical concepts, and in addition to implementation of verbal construction rules and paying attention to arabic rhythms of words, starting from concepts of noun and adjective, it derives correspondent verbal concepts from several online lexicons with analyzing of entries and text of explanations and examples in thesauruses. the rest of the paper is organized as following: section 2 present a brief introduction to farsnet and its current situation, section 3 discusses related work, section 4 describes the proposed method including verb extraction, verbal synset composition and verbal relation extraction. section 5 concludes the paper and suggests some further work. ## farsnet farsnet, the first persian wordnet @xcite is a lexical database for persian words. the first and second versions of farsnet have been established in natural language processing lab of shahid beheshti university at 2008 and 2010 respectively. farsnet 3.0 which is currently under development is expected to have 100,000 lexical entries (currently about 87,000 are available). farsnet like other wordnets is formed by a large set of lexical entries (words or phrases) organized in a network of synsets (a set of synonym terms). the edges of this network are semantic relations among synsets, including inner-pos and inter-pos ones. the relations defined between synsets in farsnet include hypernym/ hyponym, holonym/ meronym, antonym, domain, related-to, co-occurrence, cause, entails, salient-defining feature, potential-defining feature, unit and attribute; besides, some semantic roles as instrument, location, agent and patient. the report of variation trend of farsnet versions and the existing semantic relations has been presented in @xcite . also, ## related work in the related field of automatic wordnet development, several efforts have been made. according to classification of vossen (1998) for wordnet development approaches, two major approaches can be considered as merge and expansion. the merge approach is constructing a wordnet with independent use of target language resources and language specific properties and usually creating synsets from scratch, whereas the expansion method relies on existing wordnets (especially the english wordnet) and uses multilingual resources to translate words of existing synsets to target language and therefore preserves the source wordnet structure. however developing a wordnet by use of merge method is not always cost effective due to budget constraints and is more time-consuming than expansion method, it leads to a higher quality and extensive wordnet to be effectively used in certain and real nlp applications. also a wordnet developed with merge approach will reserve the target language culture and region specific concepts and semantic relations and there is no need to deal with translation ambiguity, compared to expansion approach @xcite . @xcite use a hybrid approach of merge and expansion for developing indoword-net to benefit from the advantages of both. recently, word embedding models, especially word2vec @xcite , have been the focus of much research in nlp tasks. these models are widely used to calculate semantic relatedness of words and thus they can be applied in synset construction and semantic relation extraction subtasks of a wordnet development process. the proposed work by al tarouti (2016) on arabic wordnet and @xcite on persian wordnet use vectors created by word2vec to move towards a wordnet by an expansion method. there are some other efforts to build a wordnet for the persian language by either semiautomatic or automatic methods. among semiautomatic ones we can mention @xcite , @xcite and @xcite which mainly use a merge method to build a persian wordnet. among automatic methods we can mention @xcite @xcite which mainly use expansion methods and extract some mappings between persian words and princeton synsets. these systems do not build a wordnet but can be used to initiate building a wordnet. they are good in coverage and development time but not as well in precision of result. most of the research conducted on extraction and labeling of conceptual relation for persian language (such as @xcite and @xcite ) work on a limited predefined relations such as synonymy, hyper/hyponymy and holo/meronymy relations; and they lack favorable and needed efficiency for non-taxonomic relations and those relations corresponding to semantic roles (role relations). @xcite proposes a linguistic method based on morpho-lexical patterns to extract semantic relations in order to improve the arabic wordnet (awn) performance using arabic wikipedia articles as the input corpus. the methods proposed by @xcite and @xcite carry out labeling thematic roles in sentence through rule-based approaches using shallow parsing of text; the mentioned conducted works lack favorable efficiency for extraction of conceptual relations among farsnet high-level concepts and they don't have appropriate recall either. the work done by zadeh khosravi forooshani & rezaei sharifabadi (2016) carries out semantic role labeling in persian sentences by dependency parsing; that in comparison to works implemented with shallow-parsing, has higher accuracy and better efficiency; but it does not yet propose any solution for extraction of corresponding semantic relations among high level concepts of a wordnet. the strategy suggested by @xcite is one of the works conducted for automatic extraction of persian verbal concepts in which farsnet noun and adjective concepts are used for compound verbs extraction. it considers any combination of each noun/adjective and persian light verbs as a compound verb candidate and then verifies correct words by checking up in bijankhan corpus and arianpour dictionary. then it makes verbal synsets by a rule based mothod from noun and adjective synsets. despite appropriate efficiency of this technique in derivation of phrasal verbs, with respect to reliance of this method on combination of noun and adjective with light verbs and limitation of the used lexical sources, many prefixed and propositional verbs as well as more complex expressions and verbal phrases with metaphorical concepts are not identified. ## results and conclusion this paper discusses the application of various automatic linguistic, syntactic and statistical methods on various resources to extend farsnet by a merge method. the proposed method not only has reasonable precision and coverage, but also covers culture and language specific concepts and relations which cannot be captured by expansion methods. it can either extend the existing verbal synsets by a new verb or create a new synset for new and specific verbs of persian lexicons with metaphorical meanings this strategy significantly increases recall and the number of verbs and extracted semantic relations. although it is applied to persian, it can be used for extracting and labeling semantic relations in other languages as well. the experimental results show that the proposed verb-extraction method, extracts 6890 correct verbs -regardless of polysemy and number of senses for each word and add them to farsnet 2.0-that already had 7820 verbs. the synset extraction method added 2790 verbal synsets to 3670 verbal synsests existing in farsnet 2.0. the synsets need manual judgment and semantic disambiguation of senses by lexicographers. precision for synset ex. no. of correctly extracted synsets precision for verb ex. no. of correctly extracted verbs verb extraction approach 76% 396 79% 750 applying structural rules 67% 2790 91% 6890 applying structural rules and digital lexicons the given results for automatic extraction of non-taxonomic relations contain 5600 correct relations among existing synsets in farsnet 2.0; with accuracy rate of 76%. farsnet 2.0 had 1040 semantic relations (excluding hyper/hypo-nymy, domain, and holo/mero-nymy) before applying the proposed strategy which formed only about 2.8% of the relations in farsnet 2.0. this rate reached to 15.7% after implementation of the suggested method. thus, the proposed automatic method has efficiently contributed to improve the number of non-taxonomic relations corresponding to thematic roles and co-occurrence relations and reduced size of manual processing for relation extraction. the presented strategy still leads to extraction of further and more accurate conceptual relations by increase in number of synsets and words and examples for each of the concepts by extension of farsnet.