Using Context to Improve the S panish W ord N et Translation (2018)

Abstract: We present some strategies for improving the Spanish version of WordNet, part of the MCR, selecting new lemmas for the Spanish synsets by translating the lemmas of the corresponding English synsets. We used four simple selectors that resulted in a considerable improvement of the Spanish WordNet coverage, but with relatively lower precision, then we defined two context based selectors that improved the precision of the translations.

## introduction this paper presents an approach at the expansion of the lexical database wordnet in spanish using an automatic translation processes. we implemented some previously proposed strategies for improving the coverage of the lexical database in spanish, then we analyzed the results that these strategies produced and finally we designed new strategies in order to improve the quality of the translated lemmas. the rest of the paper is structured as follows: section 2 presents the lexical database we aim to improve and describes related work in the area, section 3 describes the translation sources we used and how they were prepocessed, section 4 details the different strategies implemented for translation, section 5 shows the results obtained by the strategies and their evaluation, finally section 6 shows our conclusions and some future research directions. ## background the multilingual central repository, mcr @xcite , is a multilingual lexical database that contains linked wordnet versions for english and five languages spoken in the iberian peninsula: spanish, catalan, basque, galician and portuguese. the same princeton word-net synsets structure is used for all languages. the central component of this lexical database is the inter-lingual-index (ili), which allows the mapping of concepts of different languages through the use of identifiers. the identifiers are composed of four values: language, version of mcr, synset offset and part of speech. synsets in different languages that have the same meaning share the offset, version and part of speech, varying the language. for example, "house" (eng-30-03544360-n) corresponds to "casa" (spa-30-03544360-n) and both synsets are related through the ili code "ili-30-03544360-n". the first attempts at building a spanish version of wordnet are described in @xcite , using bilingual english-spanish dictionaries and a large monolingual spanish dictionary. a different approach is proposed in @xcite for spanish and catalan, using machine translation systems to translate the semantically annotated semcor @xcite corpus and select the translations for variants based on the relative frequencies of words in the corpus with the following strategies: in @xcite the authors present a method for improving the french version of wordnet. they compile a collection of possible translations for the variants from several bilingual sources and design strategies for selecting the appropriate translation, these strategies are called "selectors". a selector is a heuristic strategy that takes a synset and a set of candidate lemmas in the target language, and returns the most appropriate lemma that should be associated to the synset. a similar approach was followed by @xcite for the expansion of spanish wordnet, defining five selectors and obtaining good results for a subset of synsets from princeton wordnet (92% accuracy for simple selectors and 74% accuracy for the distributional selector). the selectors were only applied on a subset of the synsets due to the long execution times, also some problematic synsets (such as multiword expressions) were not considered, which might explain in part the high accuracy of the simple selectors. the authors of @xcite also use a dictionary based approach, combining several linguistic resources in a variety of languages for improving the wordnet translation in each of those languages. ## translation sources translation sources are key elements in the process of building wordnet in spanish. they provide, for the english lemmas, the lemmas in spanish that will be used by the selectors as translation candidates. two types of sources were used: dictionaries and statistical machine translators. the dictionaries are made up of tuples [english word, spanish word, pos]. they are generated manually so they are very reliable, but with a limited volume of translations. the machine translators used are statistical systems that allow to translate words and also complete sentences taking the context into consideration, a property that will be exploited by some of the selectors. ## translation process we first implemented some of the already defined selectors and applied them to the whole collection of synsets. as these selectors resulted in poor precision, we created new selectors that exploit contextual information in order to improve the precision of the translation. ## evaluation evaluation was one of the hardest tasks due to the complexity of the evaluation of some semantic notions, as well as the volume of data involved. because of this, we decided to use two methods of evaluation: evaluation by overlap and evaluation by sampling. ## conclusions we implemented four simple selectors and two contextual based selectors for the translation of english wordnet synsets to spanish, in order to expand the spanish version of wordnet present in mcr. using the simple selectors, we obtained 182,051 nouns, 19,683 verbs, 17,384 adjectives and 8,436 adverbs with 69.05% precision. the precision of these selectors was lower than the one reported in previous works, probably because in our case we evaluated the whole collection of synsets, even processing multiword lemmas. in order to improve this precision, we designed and implemented two new selectors that use the contextual information, whose execution obtained 5,339 nouns, 4,441 verbs, 6,444 adjectives and 1,747 adverbs with 82.67% precision. the context based selectors yield much fewer results because they depend on the existence of examples in the corresponding wordnet synsets. during the course of the project we detected several directions that could be explored in the future. first of all, we would need to analyze the cases in which the simple selectors did not give any results. this could mean expanding the set of translation sources in order to cover all the vocabulary of the original wordnet, as this coverage is the upper bound to what we might be able to translate. for the contextual information selectors, we could obtain a larger parallel corpus of examples. one possibility is using the semcor corpus that has been used in other projects, another possibility would be performing word sense disambiguation over a large parallel corpus, taking into account that this process would probably not select the correct synset every time. the structure selector is particularly interesting to analyze and extend, because this selector applies syntactic notions and heuristic rules that could be expanded and improved in order to add coverage and accuracy. it would also be interesting to design new selectors based on the notions of distributed semantics, such as the use of word embeddings. the relations contained in wordnet could be used to guide the selection of new lemmas given the word embed-dings property that words close in the vector space tend to have similar or related meanings.