Phonotactics as an Aid in Low Resource Loan Word Detection and Morphological Analysis in Sakha (2023)

Abstract: Obtaining information about loan words and irregular morphological patterns can be difficult for low-resource languages. Using Sakha as an example, we show that it is possible to exploit known phonemic regularities such as vowel harmony and consonant distributions to identify loan words and irregular patterns, which can be helpful in rule-based downstream tasks such as parsing and POS-tagging. We evaluate phonemically inspired methods for loanword detection, combined with bi-gram vowel transition probabilities to inspect irregularities in the morphology of loanwords. We show that both these techniques can be useful for the detection of such patterns. Finally, we inspect the plural suffix -ЛАр [-LAr] to observe some of the variation in morphology between native and foreign words.

## introduction sakha is a turkic language, with around half a million native speakers @xcite , primarily residing in the sakha republic. the sakha republic is located in northeast asia, and is part of the russian far east. sakha belongs to the lena group of the turkic language family. like other turkic languages, sakha is agglutinative @xcite . it has complex, four-way vowel harmony, and the subject-object-verb word order, which we want to use to identify loan words. its lexicon consists of turkic words, borrowings from mongolic and tungusic languages, loanwords from russian, and words of unclear (possibly paleo-asiatic) origin @xcite . note that in this project we do not draw any distinction between different types of borrowing or degree of naturalization. where not specified, "loanword" should be understood to mean nonnativized loanword. words should be understood as types, and we do not account for homography. sakha words are transliterated using the turkish ortography, expressed in brackets []. while the corpus cannot be re-distributed freely, functions and code details will be made available foot_0 . ## earlier research and motivation as the tools available to sakha, as for many other low-resource languages are rule-based, spelling inconsistencies can affect down-stream tasks. an example is the errors in inflection of loanwords during analysis of errors made both by systems submitted for sigmorphon 2021 shared task on morphological reinflection @xcite and forms generated by a morphological analyser created for sakha @xcite which was considered as the ground truth. the authors experienced that in some cases several native speakers could not agree on what should be the correct spelling. this is one of the indications of inconsistencies when it comes to vowel harmony in loanwords. for example both forms автомобилэ [avtomobile] and автомобила @xcite were found for the original russian автомобиль [avtomobil'] 'car'. other attempts at loanword identification for turkic languages include @xcite for uyghur, using word embeddings. an example of using phonemic information is @xcite , who map japanese and english loan pairs to inspect their phonology. ## sakha phonotactics and vowel harmony in addition to looking at letters used only in russian, we will exploit certain phonotactic regular-ities in sakha, namely restrictions on consonant distributions, and vowel harmony. consonant distributions while letters such as г [g] and д [d] and ь [soft sign; indicates palatalization] are present in sakha words, г and д are never found word-finally, due to the fact that all voiced sounds are disallowed in this position @xcite . while ь is found in the digraphs дь [c] and нь [ñ], its presence after any other consonant indicates a borrowing. sakha is also typically much more restrictive with consonant combinations than russian @xcite consequently we can classify all illegal consonant bigrams as loans, as well as all words containing consonant trigrams, as no consonant trigrams are allowed in sakha. we will use sakha consonantal features mainly to be able to create a rule-based classifier of words into native-like and foreign. looking at the features outlined above, we can classify with reasonable certainty a word as foreign, but we cannot class a word as native with equal possibility, as the presence of a foreign consonant or a specific pattern can quite confidently mark a word as non-native, the opposite is not true. many unnaturalized loanwords conform to sakha spelling by chance. the consonant-related features we will be looking for are the following: 1) presence of foreign letters 2) illegal consonant positions 3) bigrams 4) trigrams. vowel harmony vowel harmony is a phenomenon where the use of a vowel is dependent on vowels in its context. sakha exhibits a relatively strict, four-way vowel harmony. the vowels are shown in ## data we base our calculations on a corpus collected by @xcite . this corpus contains 21 000 newspaper articles, gathered from 2006 to 2015. the corpus contains a total of 21 million words. these texts also contain some ocr-read text, as well as latin-letter text. predictable ocr errors are corrected on reading, and latin words are removed before further processing. the resulting list of lowered, normalized tokens counts 454 190 items. ## conclusion and future work we have seen that phonotactic rules can be useful for loanword identification in sakha, and that by using this information, we can gain insight on the morphological treatment of these words. we have shown that when vowel harmony is strict, it is also a good indicator of loanwords, and we have showed how this can be used to illustrate alternations in morphology. we expect the results here to be relevant for any language with vowel harmony or similar phenomena. we would also like to stress that these rule-based methods are simple and efficient, and allow large amounts of lexicographic work and preprocessing be done on languages where preprocessing tools or lexical lists are unavailable, but some raw data exists. however, we only inspected one of many sakha suffixes, and believe that further investigations can shed light on the actual state of vowel-oriented morphological variation in sakha. we also note that although the rule-based function work well, good lemmatization techniques would be able to remove some ambiguity in our analyses.