Jatayu Baxi


2022

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UniMorph 4.0: Universal Morphology
Khuyagbaatar Batsuren | Omer Goldman | Salam Khalifa | Nizar Habash | Witold Kieraś | Gábor Bella | Brian Leonard | Garrett Nicolai | Kyle Gorman | Yustinus Ghanggo Ate | Maria Ryskina | Sabrina Mielke | Elena Budianskaya | Charbel El-Khaissi | Tiago Pimentel | Michael Gasser | William Abbott Lane | Mohit Raj | Matt Coler | Jaime Rafael Montoya Samame | Delio Siticonatzi Camaiteri | Esaú Zumaeta Rojas | Didier López Francis | Arturo Oncevay | Juan López Bautista | Gema Celeste Silva Villegas | Lucas Torroba Hennigen | Adam Ek | David Guriel | Peter Dirix | Jean-Philippe Bernardy | Andrey Scherbakov | Aziyana Bayyr-ool | Antonios Anastasopoulos | Roberto Zariquiey | Karina Sheifer | Sofya Ganieva | Hilaria Cruz | Ritván Karahóǧa | Stella Markantonatou | George Pavlidis | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Candy Angulo | Jatayu Baxi | Andrew Krizhanovsky | Natalia Krizhanovskaya | Elizabeth Salesky | Clara Vania | Sardana Ivanova | Jennifer White | Rowan Hall Maudslay | Josef Valvoda | Ran Zmigrod | Paula Czarnowska | Irene Nikkarinen | Aelita Salchak | Brijesh Bhatt | Christopher Straughn | Zoey Liu | Jonathan North Washington | Yuval Pinter | Duygu Ataman | Marcin Wolinski | Totok Suhardijanto | Anna Yablonskaya | Niklas Stoehr | Hossep Dolatian | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Aryaman Arora | Richard J. Hatcher | Ritesh Kumar | Jeremiah Young | Daria Rodionova | Anastasia Yemelina | Taras Andrushko | Igor Marchenko | Polina Mashkovtseva | Alexandra Serova | Emily Prud’hommeaux | Maria Nepomniashchaya | Fausto Giunchiglia | Eleanor Chodroff | Mans Hulden | Miikka Silfverberg | Arya D. McCarthy | David Yarowsky | Ryan Cotterell | Reut Tsarfaty | Ekaterina Vylomova
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive.In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

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GujMORPH - A Dataset for Creating Gujarati Morphological Analyzer
Jatayu Baxi | Brijesh Bhatt
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Computational morphology deals with the processing of a language at the word level. A morphological analyzer is a key linguistic word-level tool that returns all the constituent morphemes and their grammatical categories associated with a particular word form. For the highly inflectional and low resource languages, the creation of computational morphology-related tools is a challenging task due to the unavailability of underlying key resources. In this paper, we discuss the creation of an annotated morphological dataset- GujMORPH for the Gujarati - an indo-aryan language. For the creation of this dataset, we studied language grammar, word formation rules, and suffix attachments in depth. This dataset contains 16,527 unique inflected words along with their morphological segmentation and grammatical feature tagging information. It is a first of its kind dataset for the Gujarati language and can be used to develop morphological analyzer and generator models. The dataset is annotated in the standard Unimorph schema and evaluated on the baseline system. We also describe the tool used to annotate the data in the standard format. The dataset is released publicly along with the library. Using this library, the data can be obtained in a format that can be directly used to train any machine learning model.

2021

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Morpheme boundary Detection & Grammatical feature Prediction for Gujarati : Dataset & Model
Jatayu Baxi | Brijesh Bhatt
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

Developing Natural Language Processing resources for a low resource language is a challenging but essential task. In this paper, we present a Morphological Analyzer for Gujarati. We have used a Bi-Directional LSTM based approach to perform morpheme boundary detection and grammatical feature tagging. We have created a data set of Gujarati words with lemma and grammatical features. The Bi-LSTM based model of Morph Analyzer discussed in the paper handles the language morphology effectively without the knowledge of any hand-crafted suffix rules. To the best of our knowledge, this is the first dataset and morph analyzer model for the Gujarati language which performs both grammatical feature tagging and morpheme boundary detection tasks.

2015

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Morphological Analyzer for Gujarati using Paradigm based approach with Knowledge based and Statistical Methods
Jatayu Baxi | Pooja Patel | Brijesh Bhatt
Proceedings of the 12th International Conference on Natural Language Processing

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