Derivational Relations in A rabic W ord N et (2018)

Abstract: When derivational relations deficiency exists in a wordnet, such as the Arabic WordNet, it makes it very difficult to exploit in the natural language processing community. Such deficiency is raised when many wordnets follow the same development path of Princeton WordNet. A rule-based approach for Arabic derivational relations is proposed in this paper to deal with this deficiency. The proposed approach is explained step by step. It involves the gathering of lexical entries that share the same root, into a bag of words. Rules are then used to affect the appropriate derivational relations, i.e. to relate existing words in the AWN, involving part-of-speech switch. The method is implemented using Java. Manual verification by a lexicographer takes place to ensure good results. The described approach gave good results. It could be useful for other morphologically complex languages as well.

## introduction a wordnet is a lexical database built of synsets. one synset represents one concept and contains words from the same part of speech (pos) @xcite . synsets are interconnected with different relations. but, there are no cross-part-of-speech relations. this type of relation is a link between words sharing the same stem and meaning like the verb 'eat' and the noun 'eater'. the first wordnet, princeton wordnet @xcite , was built for the english language. since that, many wordnets has seen the light for over 160 languages foot_0 . one of them is the arabic wordnet @xcite (henceforth, awn) for the modern standard arabic. awn followed the development of princeton wordnet and eurowordnet @xcite . started in 2007, researches on awn are made to improve it. some of theme improved its contents @xcite . others used it in different disciplines of the natural language processing (henceforth, nlp) @xcite . despite the greatness of these works, it clearly did not take into consideration the specificities of the arabic language, especially, its morphological aspect. a lexicon, like awn, needs to have an extensive coverage, high quality, and multiple use in nlp applications @xcite @xcite . adding to that, derivational morphology provides handful information for the benefit of the nlp. as proof, wilbur and smith @xcite showed that it can be used to calculate probabilities of semantic relatedness. also, sagot @xcite used derivational analysis to determine if an unknown word can be used to create a new one for a lexicon extension. derivational morphology is used to extend different wordnets like bulgarian, serbian and romanian wordnet @xcite @xcite . the aim of this pilot study is to enrich the awn with derivational relations using a rule-based approach to extend its coverage and turns it into a more useful knowledge base. the rule-based approach includes domain knowledge into linguistic knowledge. this yield accurate results. yet, linguistic knowledge acquired for one nlp system may be reused to build others systems that require similar knowledge. those approaches are based on a solid core of linguistic knowledge. they depend on handconstructed rules from a lexicographic rather than automatically gathered from data. this paper is structured into five sections. section 2 is an overview of the awn. section 3 will provide some background on the arabic language. in section 4, we will discuss some related works. we will also discuss the choice of the rule-based approach regarding other approaches. we will speak about our approach in details in section 5. last but not least, we will show the obtained results in section 6. it contains 11,269 synsets, equivalent to 23,481 words, and 161,705 links equivalent to 22 types (5 of them for the interconnection between pwn and awn) @xcite . another version is freely available on the internet structured under the lexical markup framework (lmf) developed by @xcite . ## overview of the arabic wordnet we notice that v2 contains more synsets and fewer words then the lmf file. in one hand, v2 has 11,269 synsets related with 161,705 links, on the other hand, the lmf file 8,550 synsets related with only 41,136 links. this is not proportional to the number of the words. there are many kinds of links in awn v2. ## arabic language as it is widely known, the arabic language is a semitic language which makes it different from other languages, like english or french. it is characterized by an inflectional and derivational morphology. inflectional morphology is divided into verbal and nominal morphology. the verbal morphology bends on the aspect, the mood, the voice and the subject (person, gender, and number) of the verbs. the nominal morphology bends on the gender, the number, the state, and the case of nouns, the adjectives, and the proper nouns. the derivational morphology consists of the deverbal noun, the active participle, the passive participle and other derivations based on patterns change @xcite . all things considered, this richness provides an effective information for many nlp tasks. besides, arabic is a notable language for its nonconcatenative morphology which is the modification of the internal structure of a word. in other mdrs (teacher) foot_2 share the same arabic root d -r -s (d -r -s) which is the concept of studying. to that end, we can say that those two words are derivationally and semantically related. more details about the arabic morphology, you can found it in (habash, 2010). ## related work even though derivational morphology is a numerous area of studies, we did not found many lexical resources that rely on this kind of morphology, in the arabic language. derivational relations enrichment started with the turkish wordnet in 2004. bilgin et al. @xcite described a semi-automatic approach to add new synsets by applying derivational rules to existing words and add a morpho-semantic link between them. this type of approach is basically adding automatically suffix and prefix to a steam. since it is automatic, manually validation is mandatory and important. the same work is done to the czech wordnet @xcite . fellbaum et al. @xcite did not follow the same approach but instead, he added morphological relations between derived pairs of words in pwn. the derived pairs of words are recognized automatically since they share the same steam. manual validation is also necessary. this type of relation is cross-pos (between verb and noun pairs). in 2012, the same kind of work is followed in the romanian wordnet by mititelu @xcite . the work is summarized in two steps. the first step is to create all possible combination, given 3 lists of words, prefixes, and suffixes. the second step is to validate the affectation of prefixes and suffixes, each one aside, using a set transformation rules. the bulgarian @xcite , the serbian @xcite and the polish wordnet @xcite adopted another type of approach. based on the alignment to the pwn, the approach consists of transforming the derivational relations existing in the pwn to each wordnet. in their case of study, koeva et al. @xcite proposed several approaches to make the generating of new synsets and relations possible based on derivational patterns of different pos. outside wordnets, lefff @xcite is a morphological lexicon for french based on the lexical framework alexina. this framework is used with different languages to develop morphological and syntactic nlp lexicons like persian, sorani, kurdish and even english. this lexicon is freely available and has a large coverage. it is constructed by merging several existing resources using semi-automatic techniques and conversion. remaining with the same language, verbaction @xcite too is a morphological resource who couples verbs with their action nouns (inspect/inspection). verbagent @xcite is the same as verbaction but with agent nouns (inspect/inspector). the available evidence seems to suggest that the development of those resources is either based on manual work or validation and/or lexical information (derivational and morphological rules). other attempted researches are less supervised and based only on morphological information. can et al. @xcite proposed an unsupervised method based on different pos to produce morphological rules. bernhard @xcite described two methods for unsupervised learning of morphological families. the first one is called morphoclust. it clusters words into families using hierarchical classification methods. the second one is called morphonet. it constructs a lexical network from the words presented in morpho-clust. the words represent the nodes and the morphological relations represent the links between those words. recently in 2016, zaghouani et al. @xcite have developed the ampn, a semantic resource, based on arabic morphological patterns. it clusters the verbs of arabic propbank foot_3 @xcite according to their morphological patterns. arabic verbs are studied according to their lemmas. they are defined as a combination of root and morpheme patterns of the verbs. basically, the cited approaches rely on morphological rules. in another way, they are rule-based approaches. each one used some morphological rules specific to its language to whether generate new words (adding prefixes and suffixes) or coupling existing words (share the same steam). the advantage of this type of approach is the analysis of the input and output of a system using linguistic knowledge. also, it helps the language learner's to better understand the language. however, other approaches, like statistical-based or machine learning, cannot distinguish between well-formed or ill-formed input which is an issue in some nlp tasks @xcite . there is a rapidly growing literature on @xcite , which indicates that rule-based approach for arabic nlp tasks reported successful results. shaalan presented 4 tools and 3 systems based on arabic morphological and syntactic rules. the tools are about morphological analyzer/generator and syntactic analyzer/generator. the 3 systems are machine translation, named entity recognition, and computer-assisted language learning. the aim of this study is to show that the development of systems based on rulebased approach is feasible with languages like arabic (absence of linguistic resources and difficulties of adapting tools from other languages. . . ). all things considered, it seems reasonable to base our work on this kind of approach. next section will describe precisely each step of our proposed approach. ## our approach since there is a lack of derivational relations in awn, we will attempt to add them based on the existent words in it. the suggested approach depends on lexical entries and some transformation rules. we will gather lexical entries sharing the same root into bag of words and we will use the rules to affect the appropriate types of derivational relations. each rule is based on the pos and the patterns of the words. the following figure 1 shows an arabic word with its derived forms and each with its pattern (1, 2, and 3 in the patterns refer to the three consonants of the triliteral root). the issue under scrutiny in derivational morphology is creating new words from others. in our work, instead of creating new words we will use only words that exist in awn and try to make a connection between them. this task involves pos switch (sometimes, it is preserved and we will see how). to give an illustration, let's look at the example in ## test and evaluation we implemented the method described in the previous section using java. the first thing we did is cluster words sharing the same root in a bag of words. we notice that some nouns are tagged as an adverb so we verified the pos of each word. also, some adjectives are wrongly tagged. we corrected as many as we could. we also eliminate named entities and multiword expression because they are underivatized. for our own good, the named entities are already tagged so we only eliminated the multiword expression. we only retained nominal, verbal, and adjectival entries. the results are presented in as described in the previous section, the verb class is an important fact in the affectation of the relation. 4,275 bags contain verbs. we classified those bags according to the verb form into two classes.