Morphological Tagging in B ribri Using U niversal D ependency Features (2024)

Abstract: This paper outlines the Universal Features tagging of a dependency treebank for Bribri, an Indigenous language of Costa Rica. Universal Features are a morphosyntactic tagging component of Universal Dependencies, which is a framework that aims to provide an annotation system inclusive of all languages and their diverse structures (Nivre et al., 2016; de Marneffe et al., 2021). We used a rule-based system to do a first-pass tagging of a treebank of 1572 words. After manual corrections, the treebank contained 3051 morphological features. We then used this morphologically-tagged treebank to train a UDPipe 2 parsing and tagging model. This model has a UFEATS precision of 80.5 ± 3.6, which is a statistically significant improvement upon the previously available FOMA-based morphological tagger for Bribri. An error analysis suggests that missing TAM and case markers are the most common problem for the model. We hope to use this model to expand upon existing treebanks and facilitate the construction of linguistically-annotated corpora for the language.

## introduction it is essential that the fields of linguistics and natural language processing dedicate time and resources towards smaller, indigenous, and minority languages. building annotated and tagged corpora for smaller languages supports the expansion of nlp capabilities in processing them, and could potentially expand the languages' domain of usage and help create tools that aid in language revitalization and normalization. in this paper we worked on one small building block of future nlp tools: the morphological tagging of corpora in the bribri language, an indigenous language from costa rica. in section 1 we review the process of morphological tagging and describe the bribri language's vitality and context. in section 2, we describe an algorithm for rule-based tagging, and how we used this for our first attempt at automatic tagging. after correcting any resulting errors, we trained a deep-learning based model to perform future tagging. section 3 describes the tags applied to the treebank, compares the model's performance to a previously available tagger, and describes the errors that the model is making in its tagging output. finally, section 4 describes some limitations of the tagging scheme when describing bribri data, as well as directions of future work. ## methodology our overall goal is to improve morphological tagging for bribri. in this section we will explain how we used a rule-based algorithm to tag the existing treebank using universal dependencies features. after manual correction, we tested these features by using them to train parsing models. when those models were trained we compared their performance to that of a pre-existing morphology analysis system for bribri. ## results at the end of the tagging process, a word would have its universal dependencies' morphological features in the corresponding conll-u column. ## discussion overall, the rule-based tagging of the verbs was difficult due to their morphological complexity, and numerous manual corrections were needed. we had specific regular expressions for over 80 verbs, and so the rules described in section 2.2 would not be easily transferable to larger segments of written bribri. however, our objective in using these rules was to create a new system which could accept forms it hasn't seen before as its input. the morphological feature tags we have introduced to the treebank produce acceptable results during inference. our future work is to take this new treebank and use it to make morphological and syntactic parsings of unseen sentences of bribri in order to expand existing corpora. the most immediate item of future work is to expand the tags for the remaining parts of speech. for example, bribri's deictic system includes pronouns that refer to distance from the speaker (near, far) and vertical position from the reference point (above, even and below). for example, the word aí means 'that one (above, near)', and the word dià means 'that one (below, far)'. it also includes deictics which need the feature @xmath0, like the word ñe' 'that one (that can be heard but not seen)'. these are tags that already exist in the universal features, and should be easy to expand upon. there are also places where the parts of speech treated here could be expanded. for example, bribri has several diminutive morphemes for nouns and adjectives (e.g. amì 'mother' versus amíla 'mommy'). these would take the feature @xmath1, but this was not included in the present work. these morphemes are important for the studying of bribri discourse, and so their tagging is necessary in the future. more complex to tag are numerical classifiers. these classifiers contain the number, but also semantic information about the geometry of the object. some examples are: (i) buà b ötöm 'two @xcite iguanas', (ii) apë' b öl 'two people', (iii) àshali b ök 'two[round] oranges', and (iv) kua'kua b öt 'two[flat] butterflies'. there are at least 8 of these classes, and their tagging cannot be described with the features in universal features. that additional information would have to be included separately. finally, there is additional information about the verbs that also needs to be saved separately. for example, bribri verbs distinguish between "recent" and "remote" past perfect tenses. for example, the sentence ye' shka' means 'i walked (sometime yesterday, before i went to bed last night, or further back in the past)'. on the other hand, the sentence ye' shké is also perfect, but it covers both the immediate present perfect (e.g. 'i will walk'), and a perfect aspect action that has occurred in the recent past, after the last time one went to bed (e.g. 'i walked (sometime today, in the recent past)'. this recent tense has also been called the hodiernal tense in literature @xcite . this distinction cannot be described in the universal features, and would have to be stored separately as well. one piece of future work is to make a system that performs automatic morphological segmentation. such a system would get the input shkàne 'there was walking', and would be able to produce the output shk-àn-e, with the root shk 'walk', the middle voice suffix -àn and the remote past tense perfect aspect suffix -e. we hope that the feature tagging described in this paper will be helpful in making such a segmentation system, which would further contribute to the creation of annotated corpora. ## conclusions in this paper we have presented a new morphological tagger for the bribri language. we automatically tagged an existing treebank with universal dependencies' universal features. we handcorrected any errors during the tagging process, and then used this new treebank to train a parsing model. this model has significantly better performance than the previous fst-based analyzer. we will continue to expand upon this work, using these tools to aid in the annotation of corpora for the language.