Towards a First Automatic Unsupervised Morphological Segmentation for I nuinnaqtun (2021)

Abstract: Low-resource polysynthetic languages pose many challenges in NLP tasks, such as morphological analysis and Machine Translation, due to available resources and tools, and the morphologically complex languages. This research focuses on the morphological segmentation while adapting an unsupervised approach based on Adaptor Grammars in low-resource setting. Experiments and evaluations on Inuinnaqtun, one of Inuit language family in Northern Canada, considered a language that will be extinct in less than two generations, have shown promising results.

## introduction nlp has significant achievements when dealing with different types of languages, such as isolating, inflectional or agglutinative language families. however, indigenous polysynthetic languages still pose several challenges within nlp tasks and applications, such as morphological analysis or machine translation, due to their complex linguistic particularities and due to the scarcity of linguistic resources and reliable tools @xcite @xcite . herein, we propose an unsupervised morphological segmentation approach, which is primarily based on the grammar containing production rules, non-terminal and terminal symbols, and a lexicon using adaptor grammars @xcite . our current research investigates inuinnaqtun -a polysynthetic language spoken in northern canada, in the inuit language family. inuinnaqtun is considered as a language that will be extinct in less than two generations foot_0 . regarding the eskimo-aleut language family including the inuit, unlike words in english, the word structure of eskimo are very variable in their form @xcite . words may be very short, built up of three formative elements such as word base, lexical suffixes, and grammatical ending suffixes, or very long, with up to ten or even fifteen formative morphemes depending on the dialect. we observe there is a general tendency to increase the lexical constituents with a word-base by adding more formative elements. a single word can express the meaning of a whole sentence. moreover, morphology is highly developed and has extensive use of lexical and grammatical ending suffixes. all these linguistic aspects make the morphological segmentation task for polysynthetic languages more challenging. on the other hand, the benefit of this work helps to identify more unknown word bases by deducting from the known affixes, which in turn helps to enrich the inuinnaqtun lexicon. the global contribution consists of helping to revitalize and preserve low-resource indigenous languages and the transmission of the related ancestral knowledge and culture. the structure of this paper is described as follows: section 2 presents relevant works. section 3 describes our proposed approach. then, section 4 presents experiments and evaluations. finally, section 5 gives some conclusions and perspectives for future research. this approach has been extended in several studies @xcite @xcite for learning non-concatenative morphology, or for unsupervised morphological segmentation of unseen languages. recently, @xcite applied ag approach for the linguists with word segmentation experiments for very low-resource african languages. @xcite has applied the ag approach in an unsupervised morphological segmentation of the low-resource polysynthetic languages such as mexicanero, nahuatl, yorem nokki and wixarika. their evaluations have shown a significant improvement up to 87.90% in terms of f1-score, compared to the supervised approaches @xcite . our work examines the efficiency of the ag-based approach on inuinnaqtun, a polysynthetic low-resource inuit language. ## our approach inspired by the work of @xcite , we adapt an unsupervised morphological segmentation with the adaptor grammars (ag) approach for the inuit language family, by completing an empirical study on inuinnaqtun. the main process consists of defining (1) the grammar including non-terminal, terminal symbols, a set of production rules, and (2) collecting a large amount of unsegmented word list in order to discover and to learn all possible morphological patterns. in our work, we consider that word structures are specified in the grammar patterns where a word is constituted as one word base, a sequence of possible lexical suffixes and grammatical ending suffixes (see in order to adapt the ag scholar-seeded setting with linguistic knowledge, we have collected a list of affixes from dictionaries and websites in the appropriate language. ## conclusion in this research paper, we presented how to build the unsupervised morphological segmentation with adaptor grammars approach for inuinnaqtun, an inuit language, considered as an extremely low-resource polysynthetic language, that will be extinct in less than two generations, as described and referenced above. this adaptor grammars-based approach showed promising results, when using a set of grammar rules, that can be collected from grammar books; and a lexicon extracted from very little data. as a perspective, we intend to develop more efficient unsupervised morphological segmentation methods and to extend our research to other indigenous languages and dialects, especially the very endangered ones; with applications on machine translation and information retrieval.