Improving Wordnets for Under-Resourced Languages Using Machine Translation (2018)

Abstract: Wordnets are extensively used in natural language processing, but the current approaches for manually building a wordnet from scratch involves large research groups for a long period of time, which are typically not available for under-resourced languages. Even if wordnet-like resources are available for under-resourced languages, they are often not easily accessible, which can alter the results of applications using these resources. Our proposed method presents an expand approach for improving and generating wordnets with the help of machine translation. We apply our methods to improve and extend wordnets for the Dravidian languages, i.e., Tamil, Telugu, Kannada, which are severly under-resourced languages. We report evaluation results of the generated wordnet senses in term of precision for these languages. In addition to that, we carried out a manual evaluation of the translations for the Tamil language, where we demonstrate that our approach can aid in improving wordnet resources for under-resourced Dravidian languages.

## introduction as computational activities and the internet creates a wider multilingual and global community, under-resourced languages acquire political as well as economic interest to develop natural language processing (nlp) systems for these languages. in general, creating nlp systems requires an extensive amount of resources and manual effort, however, under-resourced languages lack in both. wordnets are lexical resources, which provide a hierarchical structure based on synsets (a set of one or more synonyms) and semantic features of individual words. wordnets can be constructed by either the merge or the expand approach @xcite . princeton wordnet @xcite was manually created within princeton university covering the vocabulary in english language only. then, based on the princeton wordnet, wordnets for several languages were created. as an example, eurowordnet @xcite is a multilingual lexical database for several european languages, structured in the same way as princeton's wordnet. the multiwordnet @xcite is strictly aligned with princeton wordnet and allows to access senses in italian, spanish, portuguese, hebrew, romanian and latin language. many others have followed for different languages. the indowordnet @xcite was compiled for eighteen out of the twenty-two official languages of india and made available for public use. it is based on the expand approach like eurowordnet, but from the hindi wordnet, which is then linked to english. on the global wordnet association website, foot_0 a comprehensive list of wordnets available for different languages can be found, including in-dowordnet and eurowordnet etc. this paper describes the effort towards generating and improving wordnets for the underresourced dravidian languages. since studies @xcite @xcite have shown significant productivity gains when human translators post-edit machine translation output rather than translating text from scratch, we use the available parallel corpora from multiple sources, like opus, foot_1 to create a machine translation system to translate the wordnet senses in the princeton wordnet into the mentioned under-resourced languages. translation tools such as google translate, foot_2 or open source smt systems such as moses @xcite trained on generic data are the most common solutions, but they often result in unsatisfactory translations of domain-specific expressions. therefore, we follow the idea of @xcite , where the authors automatically identify relevant sentences in english containing the word-net senses and translate them within the context, which showed translation quality improvement of the targeted entries. the effectiveness of our approach is evaluated by comparing the generated translations with the indowordnet entries, automatically and manually, respectively. this paper reports our first outcomes in improving wordnet for under-resourced dravidian languages such as tamil(iso 639-2: tam), telugu (iso 639-2: tel) and kannada (iso 639-2: kan). indowordnet covers official indian languages, from the major three families: indo-aryan, dravidian and sino-tibetan languages. in general, indian languages are rich in morphology and each of the three language families has different morphology structure. it was compiled for eighteen out of the twenty-two official languages and made publicly available. 4 similarly to eurowordnet it is based on the expand approach, but the central language is hindi, which is then linked to english. the indowordnet entries are updated frequently. for the tamil language, rajendran et al. @xcite proposed a design template for the tamil wordnet. in their further work @xcite , they emphasize the need for an independent wordnet for the dravidian languages, based on euroword-net. this is due the observation that the morphology and lexical concepts of these languages are different compared to other indian languages. the authors have combined the tamil wordnet and wordnets in other dravidian languages to form the indowordnet. @xcite built sentiwordnet for the odia language, which is one of the official languages of india. being an under-resourced language, odia lacks proper machine translation system to translate the vocabulary of the available resource from english into odia. the authors have created sentiwordnet for odia using resources of other indian languages and the indowordnet. although the indowordnet structure does not map directly to the sentiwordnet, instead synsets are matched. the authors used these for translation from source lexicon to target lexicon. @xcite have created a wordnet for the kurdish language, one of the under-resourced languages in western iranian language family. they have created kurdish translation for the "core" wordnet synsets @xcite , which is a set of 5,000 essential concepts. they used a dictionary to translate its literals (words), adopted an indirect evaluation alternative in which they look at the effectiveness of using kurdnet for rewriting information retrieval queries. similarly, the work by @xcite focuses on the semiautomatic construction of wordnet for the mansi language, which is spoken by mansi people in russia, an endangered under-resourced languages with a low number of native speakers. the authors have used the hungarian wordnet as a starting point. with the help of a hungarian-mansi dictionary, which was used to create possible translations between the languages, the mansi wordnet was continuously expanded. previous works did lots of manual effort to create wordnet-like resources, which was funded by public research for a long period of time. however, indowordnet is not complete and biased towards hindi, because the authors created a hindi-tamil bilingual dictionary, rather than a wordnet. as explained in @xcite , the morphology and lexical concepts of dravidian languages are different from hindi, which illustrates that the indowordnet may not be the most suitable resource to represent the wordnet for the targeted dravidian languages. to evaluate and improve the wordnets for the targeted dravidian languages, we follow the approach of @xcite , which uses the existing translations of wordnets in other languages to identify contextual information for wordnet senses from a large set of generic parallel corpora. we use this contextual information to improve the translation quality of wordnet senses. we show that our approach can help overcome drawbacks of simple translations of words without context. ## background our specific aim of this work is to generate and improve wordnets for under-resourced languages. for our task we chose the expand approach and automatically translated the princeton wordnet entries within a disambiguate context to obtain entries for the dravidian languages. ## methodology the principle approaches for constructing wordnets are the merge approach or the expand approach. in the merge approach, the synsets and relations are built independently and then aligned with wordnet. the drawbacks of the merge approach are that it is time-consuming and requires a lot of manual effort to build. on the contrary in the expand model, wordnet can be created automatically by translating synsets using different strategies, whereby the synsets are built in correspondence with the existing wordnet synsets. we followed the expand approach and created a machine translation systems to translate the sentences, which contained the wordnet senses in english to the target language ## discussion while our automatic evaluation results are a little disappointing, and this is perhaps unsurprising in the context of under-resourced languages as there is very little a data availability for these language, our manual evaluation shows that this is far from reality. evaluating using a resource such as indowordnet is always likely to be problematic as the resource is far from complete and does not claim to cover all words in the dravidian languages studied in this paper. moreover, in-dowordnet is overly skewed to the the classical words of these languages, but the majority our parallel corpus is day to day conversation texts. despite the low precision in determining the exact match to the indowordnet, our technique yields 48% for precision at 10 in manual evaluation, although the automatic evaluation considering pre-cision at 10 gave only 12%. our method relays on indowordnet for evaluation but indoword-net is biased over one particular language, which is hindi. the resulting wordnet entries, though noisy, is suitable for aiding wordnet creation for under-resourced languages. the handling of code-mixing in this paper appears to improve the quality of the proposed translation, outperforming the baseline results of wordnet entries once code-mixed was removed from data. thus we believe that the method presented here still applicable to resource creation of underresourced languages. ## conclusion in this paper we showed the challenges in building wordnet for under-resourced languages and presented that our method can aid the creation or improvement of wordnets for under-resourced languages. we experimented with available data to created smt systems for three dravidian languages and used those as a baseline. to improve the results we removed the code-mixed terms from the corpus. our results indicated that the proposed removing of code-mixed text from the corpus results in gains for the wordnet entries with limited data.