Automatic Detection of Borrowings in Low-Resource Languages of the C aucasus: A ndic branch (2022)

Abstract: Linguistic borrowings occur in all languages. Andic languages of the Caucasus have borrowings from different donor-languages like Russian, Arabic, Persian. To automatically detect these borrowings, we propose a logistic regression model. The model was trained on the dataset which contains words in IPA from dictionaries of Andic languages. To improve model’s quality, we compared TfIdf and Count vectorizers and chose the second one. Besides, we added new features to the model. They were extracted using analysis of vectorizer features and using a language model. The model was evaluated by classification quality metrics (precision, recall and F1-score). The best average F1-score of all languages for words in IPA was about 0.78. Experiments showed that our model reaches good results not only with words in IPA but also with words in Cyrillic.

## introduction field linguistics develops and practises methods for obtaining information about a language unknown (or little known) to the researcher based on work with native speakers. such languages are called low-resource languages; they represent a group of languages for which the development of information technology is insufficient. there are a number of criteria (for example, speech processing, speech recognition, automatic translation, and others) according to which experts classify specific languages as low-resource. lexical borrowings are very common to languages, including those with few resources; this phenomenon is caused by interlingual interaction and influence. if borrowings from languages with limited resources (for example, botlikh) are effectively identified, then automatic detection of borrowings with a universal base for related languages can be created and used. this article studies the method of identifying borrowings in low-resource andic languages on a linguistic basis. it implies that the model imitates the borrowing rules in the receiving language based on identifying the most relevant n-grams and generating words based on the identified borrowing patterns. many tools for working with andic languages are currently being developed, such as morphological parsers. even though each language is unique and has linguistic properties, all of them are underprivileged and endangered, as the number of their speakers is constantly decreasing, and transmission from generation to generation becomes unstable. that makes developing any nlp tools essential as it can help in their further exploration and potential revival. in addition, the detection of borrowings will help to study the language more deeply and try to preserve its identity. in the future, the work could be used to create a universal transliteration so that as many linguists as possible could work with languages and, for example, with texts. the paper's main goal is to explore the possibility of automatic borrowing detection without the usage of a bilingual dictionary since automation can contribute to future field studies of target languages. the limited amount of available data complicates the situation by reducing the number of possible analysis methods that can be implemented. the first task was to analyze the existing dictionaries. the analysis showed that the dictionaries had duplicates, which were later removed. after removing duplicates, the general borrowing rules were determined and a baseline was written with further verification of its quality. the next step was to calculate and describe insights that helped to improve the quality of the baseline. as a result, previous steps helped to cope with implementing a language model for generating additional features. to assess the quality, it was necessary to perform tasks such as writing a quality metric for the language model and statistical analysis of features. these steps will be discussed in more detail in the following sections. the rest of the article is structured as follows: the second section briefly overviews the target languages and their problems. after that, the review of the relevant literature in computational linguistics continues. the third section describes the methodology and strategies that have been used to implement the structures of each language. the fourth and fifth sections evaluate and discuss the results obtained from the available language data. the conclusion also discusses the problems that have occurred in working on the model, as well as a short description of plans for the future. ## results in addition to learning words in the ipa, the model was also trained on lemmas (bflmlem). this experiment was carried out to compare the purity of words written in phonemes and graphemes. as a result, it turned out that the quality of the model is higher than bflm (baseline with selected features and the language model) on ipa. hence, the bflm will work well for words written in ipa and cyrillic both. a comparison of the models implemented in the article, according to the f1-score metric, is presented in we compared our models with others mentioned in related works. we calculate mean precision, recall, and f1-score metrics from our experiments and the results in other articles. comparison shows that our models work slightly worse than the others, but scores remain high. hence, simple models with feature extraction based on linguistics knowledge, such as knowing about oov, can show results close to complicated neural network architecture models. models' comparison is presented in in addition to the experiments, we tested bflmlem on random letters and numbers. we got 0.93 mean accuracy of language models. besides, we examined bflm trained on ipa on english words and got 0.51 mean accuracy. ## discussion in continuation of the idea of assessing perplexity in words, neural network models can be used in the future. a recurrent neural network is perfect for this. the neural network can be trained on borrowings and then generate new words and find specific patterns. the dictionary does not fully reflect the quality of the model since it does not consider various morphological features, such as declension. for this reason, the model must be tested on work with texts. this way, it will be possible to take each word in context and determine whether it is borrowing. on the other hand, texts in languages are not presented in ipa but are written in cyrillic. in this case, it will be possible to use the epitran tool, having previously written the rules for converting graphemes to phonemes @xcite . in addition to the problem with the transformation, there is also the possibility that word declensions will also negatively affect the model. in general, this approach will show the actual quality of the model and can further help field linguists. now the model works for each language, classifying the words in it as borrowing. in the future, it may be worth refining the model, adding to it not only a binary classification but also a definition of the language from which the borrowing occurred. in this case, the problem can in this paper, we proposed methods that can be used in a borrowings detection task. it is possible that our findings might be implemented in other models which find borrowings in low-resource languages. besides, detected borrowings by the model might be helpful for field linguists working with andic languages to understand deeply these languages. ## conclusion this article has shown how to solve the problem of classifying borrowings in andic low-resource languages. for this, a baseline was first used, consisting of logistic regression and tfidf of the vectorization model. due to unsatisfactory results, the vectorization model was changed from tfidfvectorizer to countvectorizer, and hyperparameters were selected for it. in addition, a simple model based on implicit language knowledge was written. after combining these models, the quality has improved significantly. as a result, our models have scores close to neural network solutions. hence, simple binary classification can be used in tasks such as detecting borrowings. however, since the model solves a binary classification problem, it cannot tell the origin of the borrowing. in the future, it is planned to supplement the model by teaching it to solve either the problem of multiclass classification or bio-encoding. for these problems, the future models can be based on the already implemented. code and research are available on the github repository 1 .