Augmenting F ars N et with New Relations and Structures for verbs (2016)

Abstract: This paper discusses the semantic augmentation of FarsNet - the Persian WordNet - with new relations and structures for verbs. FarsNet1.0, the first Persian WordNet obeys the Structure of Princeton WordNet 2.1. In this paper we discuss FarsNet 2.0 in which new inter-POS relations and verb frames are added. In fact FarsNet2.0 is a combination of WordNet and VerbNet for Persian. It includes more than 30,000 lexical entries arranged in about 20,000 synsets with about 18000 mappings to Princeton WordNet synsets. There ae about 43000 relations between synsets and senses in FarsNet 2.0. It includes verb frames in two levels (syntactic and thematic) for about 200 simple Persian verbs.

## introduction the persian language, also known as farsi, is a member of the iranian group of the indo-iranian sub-family of the indo-european languages. it is the official language of iran, afghanistan and tajikistan with more than 100 million speakers. in persian verbs are the main carriers of a sentence meaning like many other languages. they may appear in simple or complex forms. simple verbs have simple morphological structure, the verbal constituent. compound verbs, on the other hand, consist of a nonverbal constituent, such as a noun, adjective, past participle, prepositional phrase, or adverb, and a verbal constituent. in this paper we focus on the new relations and structures added to persian wordnet (farsnet) for verbs. in the rest of the paper we first have an overview on farsnet, the persian wordnet and its features in the last two versions. section 3 talks about verb argument structures and frames. section 4 discusses the developed corpus management sys-tem in which argument structures are extracted and tagged. section 5 is dedicated to results and discussion and at last section 6 concludes the paper. ## farsnet: the persian wordnet farsnet project was announced with the release of farsnet 1.0 at 2008. farsnet 1.0 included the lexical, syntactic and semantic knowledge about more than 17000 persian words and phrases organized in about 10000 synsets of nouns, adjectives and verbs. it was a medium scaled word-net like the arabic one (at that time). as it can be seen for each word in farsnet 1.0 we have an average of 1.5 senses and each synset includes an average of .1.7 words. farsnet 1.0 was developed by a semiautomatic approach. the base concepts covered in farsnet were chosen from the base concepts bcs1 and bcs2 of balkanet @xcite with an equivalent in persian to achieve compatibility with other wordnets. and also from the most frequent words of two persian corpora: peykareh (bijankhan, 2004) and pldb @xcite to preserve the persian specific structures @xcite . farsnet 1.0 had two main classes of relations defined: inner language and inter-language relations. synonymy, hypernymy and hyponymy, different types of meronymy, antonymy and cause were among the inner-language relations. the second class included the relations equal-to and near-equal-to between farsnet and wordnet 3.0 synsets as inter-language relations. all innerlanguage relations were inner-pos; which means that their domain and range were from the same pos category. in other words farsnet 1.0 did not cover inter-pos relations. at 2010 a major restructuring of farsnet began which resulted in farsnet 2.0. the main goals of the changes were enlarging the size (improving the quantity) from the synset relations, in addition to hypernym (as between peach and fruit), hyponym (as between food and hamburger) , various types of meronym (as between apple and apple juice) and holonym (as between car door and car ) entailment (as between snore and sleep) and cause (as between kill and die) which were all present at farsnet 1.0 as well as at princeton wordnet @xcite , farsnet 2.0 includes the following relations: -has-attribute / is-attribute-of: the relation between a quantitative adjective and the attribute whose value is the adjective. for example the relation between heavy and weight or between warm and temperature -domain / is-domain-of: the relation between a domain specific term and its corresponding domain. for example between carbid and chemistry or between arthritis and medicine. -agent/ is-agent-of: the relation between a predicate (verb) and the potential agent of it. for example between author and writing or chef and cooking. -patient/ is-patient-of: the relation between a predicate (verb) and the potential patient or theme of it. for example between eat and edible thing or write and letter. -instrument/ is-instrument-of: the relation between a predicate (verb) and the potential instrument of it. for example between eat and spoon or write and pen. -corresponding adjective: the relation between an adjective and the noun it often/ mainly describes. for example the relation between stale and bread. -'related to'the relation between any two synsets which has a semantic relation other than the previous named relations. for example the relation between author and book or between school and teaching. the above relations except the "domain/is domain of" and "has attribute/is attribute of" are new to both farsnet and princeton wordnet. their creation is motivated by various nlp tasks. for example the relations between a predicate and its arguments such as agent, patient and instrument help semantic role labelers, word sense disambiguation (wsd) modules and information/ knowledge extraction systems to better find the corresponding relations and do their jobs. "related-to" relation is used to relate any two synsets which has a sort of relation not included in the above named relations. although the relation between some of the related concepts could be extracted by traversing the links in princeton wordnet or farsnet 1.0, the new relation specifies the important ones explicitly. it is mostly used in information retrieval and also in finding similarity between text components for example in text summarization.  new structure-farsnet 2.0 is actually a combination of persian wordnet and persian verbnet. it includes the verb frames (argument structure) of about 200 persian simple verbs along with the selectional restrictions of their arguments. in the rest of the paper we discuss this new feature in more details. ## the corpus management system to extract the verb argument structures we developed a corpus management system (cms) called samp @xcite . samp like other corpus management systems (such as bncweb) is able to receive a corpus as input, search in it and find and show all occurrences of a word along with its surrounding words in the corpus and prepare various types of reports about it. besides the above ordinary capabilities of a corpus management system, samp has the following features:  samp accepts any persian corpus, and changes its format to the desirable standard.  samp is a web based system capable of handling multiple synchronous users enabling cooperative corpus tagging. it creates a log of users' activities over the net.  samp is able to tokenize a raw corpus and tag it by pos categories either automatically or help to tag manually.  samp helps users to tag the corpus by senses provided by farsnet or user. in fact, samp provides a cooperative environment to let users tag the corpus semantically by farsnet senses or by new user defined senses.  samp is able to search for a word and all of its inflections, derivations and also multi part words in which the search keyword is involved. for each search the word within its surrounding context is returned. the size of the surrounding window can be determined by user.  samp helps users to tag sentences by their verb's syntactic and thematic arguments.  samp helps the linguist to extract the verb frames and determine the selectional re-strictions of arguments. actually, it recommends the verb frames by summarization and generalization (mining) of tags users created for verbs and their arguments and let the linguist to confirm or correct it (more details in the next subsection). ## results and discussion in this paper we talked about some new features developed in farsnet 2.0. besides extending the persian wordnet we have had some studies (corpus based) on verbs. in this study we selected 187 simple distinct persian verbs. for these verbs, we extracted about 4118 distinct evidence sentences from the corpus and tagged them by the meaning (sense) of verb and its arguments. from these sentences we extracted 847 sets of verb-sense-argument structure which are all entered into farsnet 2.0. in other words we completed the information of 187 verbs in farsnet with their verb frames. considering that each verb has some senses and each sense may have more than one frame we entered 847 verb frames with their selectional restrictions into farsnet. to extract the arguments we considered the valency of verbs too. valency refers to the capacity of a verb to take a specific number and type of arguments. our study showed that there is no zero-valence verb in persian. the statistics of the studied 190 simple verbs regarding their valence is shown in