Building the M ongolian W ord N et (2019)

Abstract: This paper presents the Mongolian Wordnet (MOW), and a general methodology of how to construct it from various sources e.g. lexical resources and expert translations. As of today, the MOW contains 23,665 synsets, 26,875 words, 2,979 glosses, and 213 examples. The manual evaluation of the resource1 estimated its quality at 96.4%.

## introduction language resources are crucial in the research of computational linguistics e.g., information retrieval, document classification, query answering. in recent years, world languages are divided in two groups: highlyresourced languages (e.g., english or chinese) and under-resourced languages (e.g., kazakh or uyghur). due to the lack of language resources, the second group of languages displays more mediocre performance than the first group. mongolian was one of the underresourced languages. this paper describes a general methodology by which we built the mongolian word-net (mow), a high-precision wordnet-like lexical resource. our main technical contributions are (1) a general method to extract highprecision wordnet translations from a bilingual dictionary, (2) a medium-scale lexical resource for the mongolian language. the paper is organized as follows. section 2 presents state-of-the-art methods. section 3 provides the main methodology how the mow is built, and section 5 describes the automatic algorithm to extract the wordnet translations from a bilingual dictionary. we evaluated the results of this method in section 6. finally, section 7 concludes the paper. ## state of the art princeton wordnet (pwn) has been a primary lexical resource for most researches involved in lexical semantics, from computational linguistics to semantic web. examples of particular applications are word sense disambiguation @xcite and ontology research @xcite . this successful case for english inspired many researchers to build wordnets for other languages. given the awareness of the structural and semantic diversity across languages @xcite , mono-lingual wordnets have been developed in two ways: the expansion method from pwn and the merge method with pwn. to our knowledge, a vast majority of the wordnets have been developed by using the expansion method @xcite , while very few wordnets including open dutch figure 1 : a screenshot of validator user interface wordnet @xcite , hindi word-net @xcite , polish wordnet have used the merge method. the obvious obstacle is the cost of human labor and the deep expertise of several different domains and cultures, needed in the development of a semantic network. researchers in comparative linguistics state that the semantic space of languages are vast and very differential from one another @xcite ) @xcite . this is because of the differences between speakers of languages, e.g., culture, geographic environment. this is the primary condition underlying the actual choice of the merge method because of the importance of individual culture is a fundamental to their wordnet-like lexical resource. early linguists @xcite revealed that an universal structure of lexical semantics exists across all languages at least between basic concepts, and it is why the majority of wordnet developers selected intuitively the expand method. later on, the global word-net association recommended that the monolingual semantic network should be extended by adding cultural synsets under the coordinated usage of the global wordnet grid between wordnets @xcite . ## methodology in terms of wordnet development, we adopted the expansion method. in the future, we are planning to change and expand the core semantic structure by adding more cultural concepts under the coordination of the global wordnet grid @xcite . our wordnet project has two main stages of development: (1) expert translation and (2) automatic translation. in the expert translation, the project has been running since 2016 by employing only expert linguists to translate pwn to mongolian (section 4). in the automatic translation, we have used a freely, available bilingual mongolian dictionary to translate pwn to mongolian (section 5). ## expert translation the expert translation method generally follows ontology localization @xcite which adapts an existing ontology in a language to another by using translation of terms. in this method @xcite @xcite , recruited linguistic experts and asked them to provide synsets, in the target language that properly represent a concept denoted by a synset in the source language. the main idea is to find out the most suitable words for the concept in terms of linguistic context use rather than word-for-word translation between synsets. this method consists of two main tasks: a) translation and b) validation. in the translation task, a language translator provides synset words, its gloss, and example sentences in the target language after she fully understands the meaning of a given synset to localize. if the translator assumes the concept does not exist in the target language, she should mark it as a lexical gap, which means a free combination of words represents the concepts. in this way, we avoid literal translations which may produce a wrong or unwanted result. in the validation task, a language validator evaluates all the elements of the given synset, provided by the translator. the validator either confirms each element or rejects elements one by one with feedback. in the case of a lexical gap, she can accept as it is or suggest word(s) for the synset where she denies it as a gap. when the translator receives feedback, he/she accommodates comments if she agrees with the validator. alternatively, she can reject the evaluation with comments. upon reaching an agreement between the translator and the validator, we believe this process produce target language synset with high-quality at the end. tasks for translators and validators are assigned by a language manager who manages overall translation activity. tasks are grouped into a subset of wordnet hierarchy, called subtree, which allows the linguistic experts to understand what they translate/validate. it helps to differentiate concepts by exploring their hyponym/hypernym or sibling relations. the walk-through of tasks is breadth-first. the linguistic experts use an expert sourcing tool whose screenshot of a validation process is shown in figure 1 . several volunteered @xcite and paid experts with this tool produced 12,141 synsets, 24,277 senses, and 12,830 words so far. ## automatic translation given the two resources pwn and bilingual dictionary below, the main task is to find automatically a set of pairs @xmath0, @xmath1. our method in algorithm 1 is based on the multiple intuitive criteria: the algorithm is structured with three main steps as follows. ## results and evaluation pwn has 133974 english words and then given in input to the algorithm 1, which, in turn, generated two sets of 3652 synsets and 7872 synsets from the two automatic methods of hypernym translation and monosemy translation respectively. for each of the three translations, 200 cases were randomly selected, which were equally selected across four parts of speech. three linguists were selected to evaluate the samples. they were also provided with the corresponding english glosses and words for the synsets involved, and they were asked the following question: "do you think meanings of the english synset s e and the mongolian synset s m are equivalent?", and they had to provide a yes/no answer. the mongolian wordnet now contains 23665 synsets, 40944 senses, and 26857 words as a result of the combination of all the above methods. as can be seen from ## conclusion we described how mongolian wordnet is created by using three types of translation: expert, monosemy, and hypernym-based translations under the expansion method of pwn. our main goal was to create a high-quality lexical resource, so that in automatic translations, we only selected the intuitive patterns (monosemy and hypernym) which are ensuring high quality in principles. mongolian wordnet contains 23665 synsets, 40944 senses, and 26857 words. there are 15976 nouns, 3791 verbs, 601 adverbs, and 3037 adjectives. in addition, it has 213 examples and 2976 glosses. the average polysemy is 1.52. the resource is delivered in the tab-separated format @xcite under the cc by-nc-sa 4.0 license foot_1 .