T urkish W ord N et K e N et (2021)

Abstract: Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through interlingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.

## introduction information regarding words and meanings are traditionally stored in dictionaries. with the advancement of natural language processing studies, a need for machine-readable dictionaries has arisen @xcite . in an attempt to answer that need, wordnets which store lexicographic information in a format that is adaptable to modern computing have emerged. wordnet, in its broader definition, is a highly comprehensive dictionary that is built on distinct word senses along with their definitions. most of the words in a wordnet are open-class words such as nouns, verbs, adjectives and adverbs. main building blocks of a wordnet are synsets, which are comprised of synonym synset members. synsets are the distinct units in wordnets and all the mappings including intra-and inter-lingual ones are constructed based on the synsets. in lexical semantics, it is argued that words can be defined based on the relations between them. adopting this principle, wordnets map semantic relations such as hypernymy, meronymy or antonymy through synsets. turkish wordnet of balkanet (tr-wordnet) @xcite is the first wordnet created for turkish. tr-wordnet of balkanet includes 14,626 synsets and 19,834 internal semantic relations. in this paper, we present our work on creating a more comprehensive wordnet for turkish, namely kenet foot_0 , foot_1 @xcite and discuss the creation of semantic relations such as hypernymy. we present the literature review on wordnets for other languages in section 2, describe the process of synset construction in kenet in section 3, present intralingual semantic relations including hypernymy in section 4, explain the interlingual mapping of kenet to princeton wordnet in section 5, summarize the challenges we have encountered in all these processes in section 6 and conclude in section 7. ## literature review constructing a wordnet, whether from scratch or by expanding a previous one, is a labor intensive process that requires several steps and ex-tensive use of both human labor and automated systems. since the creation of the first wordnet princeton wordnet (pwn) in 1995 @xcite , many other wordnets have been created for several languages (e.g., finnish wordnet finnword-net @xcite , polish word-net @xcite , norwegian word-net @xcite , danish wordnet @xcite , french wordnet wolf @xcite ). in addition, multilingual wordnets linking the wordnets of multiple languages have been created. to exemplify, eurowordnet (ewn) is a multilingual wordnet project that consists several european languages (english, dutch, italian, spanish, german, french, czech and estonian) @xcite . in ewn, the wordnets were created for each language separately and then linked through an inter-lingual-index based on pwn. balkanet, similar to ewn, is a multilingual wordnet project consisting of six balkan languages (bulgarian, czech, greek, romanian, serbian, and turkish) @xcite . this project was done to produce a multilingual semantic network, fully compatible with ewn and its extensions. 3 different approaches were adopted in creating wordnets and mapping them with those for other languages. two of the most-commonly used approaches in the literature are expand approach and merge approach. in the expand approach, a set of synsets from pwn, including their semantic database, are first translated into the target language and then the relations that are transferred from english are checked in a manual fashion. for example, the finnish wordnet finnwordnet @xcite and the french wordnet wolf @xcite were constructed with this approach. in the merge approach, on the other hand, the first step in creating a new wordnet is to build the intralingual relations from scratch, without any links to english. the monolingual wordnet is then mapped onto english. exemplary wordnets that were created with this approach are polish wordnet @xcite , norwegian wordnet nornet @xcite and danish wordnet dannet @xcite . in a comparison of these two approaches, it is argued that the expand approach is more practical and less time-consuming since it enables us to have many correct monolingual relations that are extracted from pwn automatically. this automatic extraction of relations from pwn is especially beneficial for languages which show a similar pattern to english in their semantic associations, such as in the case of french @xcite . in the following sections, the details in creating kenet is presented. ## synset construction the very first step in constructing kenet, as in every other wordnet, was to create synsets. synset can be defined as a group of words sharing the same sense and part of speech (pos). the structure of a sample synset in kenet is as follows: an exemplary set of synsets from kenet is given in regarding the construction of these synsets, the first version of the database was constructed through mining of the latest contemporary dictionary of turkish (cdt) (2011's print) published by the turkish language institute (tli) @xcite . by convention, cdt marks synonyms by using commas such that synonyms of a word are given after its definition with a separation of comma. to decide on true synonyms that must occur in the same synsets, we sliced the definitions at commas and listed the comma-separated lemmas and the rest of the definitions as candidates of synonyms. then, those lists were displayed for although the vast majority of the synsets were constructed during this process, there was a need for follow-up procedures to improve the organization of the current synsets. since the main problem encountered in synset construction was the semantic relatedness of the synset members, two other procedures were followed in order to control the synonymy relations within the synsets: the merge process and the split process. these two processes are discussed next. ## semantic relations currently, there are 77,330 synsets, 109,049 synset members and 80,956 distinct synset members in kenet. the pos categories that are included are nouns, adverbs, adjectives, adverbs, interjections, pronouns, postpositions and conjunctions (see in kenet, eight intralingual semantic relations were included: hypernymy, derivational relatedness, domain topic, part holonymy, antonymy, instance hypernymy, member holonymy, substance holonymy and attribute (see there can be various approaches to constructing semantic relations in a wordnet such as translating an already constructed wordnet from another language into the target language or building one from scratch. both approaches have their advantages and disadvantages, which will change drastically from one language to another. translating a previously constructed wordnet into another language, while seems to be the easier approach, comes with a lot of disadvantages, especially in languages like turkish which are morphologically and syntactically different from english. in kenet, in the creation of all these eight semantic relations, we consulted the semantic relations in the english wordnet pwn, but none of the relations were automatically translated from english. that is, in constructing the semantic relations, possible relations between turkish synsets were listed based on their dictionary translations in english pwn and the relations between the english synsets in pwn. then, human annotators checked these relations manually; the correct relations were added to kenet whereas the incorrect ones were eliminated. for example, as ## interlingual relations with the creation of wordnets in several languages, the idea of matching these wordnets to english and/or to one another has gained importance since the linking of wordnets across languages would enable us to use these resources in machine translation. as discussed in section 1, there are two available wordnets for turkish, which are tr-wordnet of balkanet and kenet. having been created as part of balkanet @xcite , tr-wordnet of balkanet was integrated to pwn through an interlingual index (ili) @xcite . as opposed to tr-wordnet, in our work, we used the merge approach and matched the synsets in kenet with those in pwn manually @xcite . additionally, as in building intralingual semantic relations, we extracted candidate multilingual relations based on dictionary translations and listed these potential interlingual relations on google sheets. two human annotators checked the accuracy of these candidate relations one by one. in tr-wordnet of balkanet, only one-to-one mappings between turkish synets and the english synsets in pwn were included due to the use of ili @xcite . in kenet, on the other hand, although one-to-one matchings made up the majority of the interlingual relations between turkish and english, one-to-many mappings between the two languages were also included @xcite . one-to-many mappings between kenet and pwn were mainly used when selecting a single synset in one of the languages was not possible. that is, the two most common cases where one-to-many matchings were needed were (i) when a sense distinction in english is not reflected in turkish, e.g., the english synsets "inequitable unjust", "unfair unjust", "unrighteous", "undue unjustified unwarranted" and "unlawful wrongful" all correspond to haksız nahak in turkish or (ii) vice versa; when a sense distinction that is present in turkish is not reflected in english, e.g., the turkish synsets hafiflemek, hafiflemek azalmak and kırılmak yatıs ¸mak all correspond to the english synset "abate let up slack off slack die away". overall, the inclusion of one-to-many matchings in kenet allowed for a better matching between the two languages.at the end of the manual interlingual matching between kenet and pwn, 19,398 synsets in turkish were associated with 19,208 synsets in english. 3,500 were one-to-one and 1,250 of them were one-to-many matchings. furthermore, out of 5,000 most frequent senses in english, 4,417 (88%) were matched with their turkish equivalents in order to have the matchings of the synsets that are most commonly used. ## challenges we have faced many resource-related and language-related challenges in creating kenet. we faced an important resource-related problem in the creation of synsets due to not having a turkish dictionary that marks the synonymy relation in a systematic fashion. we also encountered language-related problems in constructing synsets. for example, some synsets in kenet included synset members with different pos categories. the reason for this was that in turkish some words can be used in different pos categories with similar meanings. this resulted in wrong groupings of synonyms, which we had to deal with in the split process. in building the hypernymy relations, one of our biggest challenges was that some synsets in pwn did not have corresponding synsets in kenet. when this was the case in the upper parts of the tree, we came up with new synsets that would connect the lower parts of the tree with the upper parts as the tree would otherwise be missing some transitionary nodes. moreover, in building interlingual relations, we realized that having only one-to-one mappings would not be a correct matching for turkish and english and hence, we decided to include one-to-many mappings, which was a time-consuming process to conduct. lastly, we had to devote a huge amount of time and labor in all the stages in creating kenet as in most of the stages, we could not refer to earlier work and conducting the stages automatically would be misleading, thus human annotators had to work manually. this was mainly due to the structural and lexical differences between turkish and other highly-investigated languages such as english. ## conclusion in this paper, we presented the process for creating the turkish wordnet kenet and discussed the challenges that we encountered. our biggest challenge was to work on a low-resource language since most of the studies in the field focus on highlyinvestigated languages like english. the differing morphological and syntactic properties of turkish also prevent us from adopting the exact approaches used in these studies. although structural differences have created problems mostly for morphological analyses, we also encountered cases where semantic mappings and/or relations in english could not be directly copied to turkish. this discrepancy was observed in both intraand inter-lingual semantic relations. overall, unavailability of sources which can be easily linked to turkish forced us to include a huge amount of manual annotations. kenet has been used as a source in other nlp studies on turkish such as turkish propbank tropbank @xcite , turkish sentinet hisnet @xcite , turkish framenet @xcite and domain wordnets for estate @xcite and tourism @xcite . having a common wordnet source for different nlp studies in a given language can be a great advantage for the linking of these sources. that is, when the sense categorization between two different sources of a given language do not match well, as in the case of english propbank and english wordnet pwn @xcite , the linking of the available sources becomes more challenging.