FII _ CROSS at S em E val-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (2021)

Abstract: This paper presents a word-in-context disambiguation system. The task focuses on capturing the polysemous nature of words in a multilingual and cross-lingual setting, without considering a strict inventory of word meanings. The system applies Natural Language Processing algorithms on datasets from SemEval 2021 Task 2, being able to identify the meaning of words for the languages Arabic, Chinese, English, French and Russian, without making use of any additional mono- or multilingual resources.

## introduction the computational task of disambiguating a word in the context of its sentence is still a very challenging topic facing natural language processing (nlp). in this study, we refer to word meaning that requires a multidisciplinary approach for its detection. from sense-based and contextualized embeddings, all tries are aimed at providing an understanding of words in context. we notice that evaluating such approaches is not easy. for instance, traditional word sense disambiguation (wsd) fails to test latent representations unless these are linked to explicit sense inventories such as wordnet @xcite or @xcite . to resolve the problem of disambiguation for both lingual dimensions, we tried to use a combination of well-known algorithms to provide an optimal system. the legitimate research questions this paper intend to answer: is word-in-context disambiguation a barrier for nlp techniques? the approach we propose in this paper investigates two models of cross-lingual word embeddings, comparing them to the sharedtranslation effect and the cross-lingual coactivation effects of false and true friends (cognates) found in human language. we find that the similarity structure of the cross-lingual word embeddings space yields the same effects as the human bilingual lexica @xcite . research on bilingual lexica has uncovered fascinating interactions between the l1 (native language) and l2 (second language) lexica showing that both production and comprehension coactivate lexical items in both languages, indicating that bilinguals store lexical representations from their native and their second language in the same space. the rest of the paper is organized as follows: section 2 describes the literature related to sense disambiguation, section 3 presents the dataset and method of this study, section 4 resumes the results of the conducted experiments, followed by section 5 with the conclusions and discussions about how to increase the accuracy. ## background this topic has attracted significant attention in recent years, evidenced by increasing number of workshops (e.g., semeval-2013 task 10: crosslingual world sense disambiguation -clwsd). participating in such competitions is especially attractive since teams have thus access to labeled data. for binary tasks, as the case of this competition, there are many computational models to be used in detecting the right word sense. the recent advancements in corpus linguistics technologies, as well as the availability of more and more textual data, encourage many researchers to take advantage of comparable and parallel corpora to address different nlp tasks. work on this topic is however highly subjective and biased. in general, the methods are based on bag of words features, usually normalized with tf*idf or character n-grams features for stylistic purposes. most approaches are supervised methods which can be classified into different methods: also, several systems use an approach similar to ours' in considering sent2vec the main algorithm @xcite . other systems used a maxent classifier trained over local context or even a knn (k-nearest neighbors) classifier to solve the clwsd (cross-lingual word sense disambiguation) task. one of the interesting approaches was using machine translation @xcite . although the winning systems for the clwsd task used different approaches (statistical machine translation and classification algorithms), they also only used a parallel corpus to extract disambiguating information, while not using external resources such as wordnet. as a consequence, their system is very flexible and language-independent. the topic of multilingual and cross-lingual disambiguation has attracted significant attention in recent years @xcite , with approaches ranging from learning effective vector representations @xcite to infusing neural networks with knowledge graph information @xcite . our approach is more focused on recent research on the bilingual lexicon, which uncovered fascinating interactions between the lexica of the native language and that of the second language in bilingual speakers. thus, it has been found that the lexicon of the underlying native language affects the organization of the second language (riley et al., 2020). in that spirit, our system includes distributed representations to disambiguate words in context. ## datasets and methods the dataset for the semeval-2021 task 2: multilingual and cross-lingual word-in-context disambiguation is detailed in the task description paper @xcite . ## results the results for each individual task (precision, recall and f1-score) using the specific test dataset are presented: for multilingual test dataset ( ## conclusion and discussions this paper presents a system participating at semeval 2021 task 2. our solution indicates a good start for solving word sense disambiguation. the main challenge behind word sense disambiguation is to make ample use of the available technologies since ambiguities in any language provide great difficulty in the use of information technology. the major difficulty lays in the fact that words in human language can be interpreted in more than one way, depending on the context @xcite . since we performed a detailed investigation of monolingual and bilingual disambiguation, our experimental results showed that sent2vec and lesk approaches are remarkably efficient for both tasks. the overall results are satisfactory and exceed the baseline; however, there is still room for improvement. a larger and well-annotated dataset would provide more opportunities for exploring the issue of disambiguation. additionally, building a dataset sufficient in size and diversity will allow experiments with deep learning methods. the biggest challenge in this project was working in different languages at the same time while some tools were available to english only. from our research, we noticed that the average scores are around 75% when applying separately lesk and sent2vec (or even word2vec), and it seems to be similar when combining the two.