This paper presents the development of the Parallel Universal Dependency (PUD) Treebank for two Indo-Aryan languages: Bengali and Magahi. A treebank of 1,000 sentences has been created using a parallel corpus of English and the UD framework. A preliminary set of sentences was annotated manually - 600 for Bengali and 200 for Magahi. The rest of the sentences were built using the Bengali and Magahi parser. The sentences have been translated and annotated manually by the authors, some of whom are also native speakers of the languages. The objective behind this work is to build a syntactically-annotated linguistic repository for the aforementioned languages, that can prove to be a useful resource for building further NLP tools. Additionally, Bengali and Magahi parsers were also created which is built on machine learning approach. The accuracy of the Bengali parser is 78.13% in the case of UPOS; 76.99% in the case of XPOS, 56.12% in the case of UAS; and 47.19% in the case of LAS. The accuracy of Magahi parser is 71.53% in the case of UPOS; 66.44% in the case of XPOS, 58.05% in the case of UAS; and 33.07% in the case of LAS. This paper also includes an illustration of the annotation schema followed, the findings of the Parallel Universal Dependency (PUD) treebank, and it’s resulting linguistic analysis

In this paper, we discuss the development of treebanks for two low-resourced Indian languages - Magahi and Braj - based on the Universal Dependencies framework. The Magahi treebank contains 945 sentences and Braj treebank around 500 sentences marked with their lemmas, part-of-speech, morphological features and universal dependencies. This paper gives a description of the different dependency relationship found in the two languages and give some statistics of the two treebanks. The dataset will be made publicly available on Universal Dependency (UD) repository in the next (v2.10) release.

This paper enumerates SigTyP 2020 Shared Task on the prediction of typological features as performed by the KMI-Panlingua-IITKGP team. The task entailed the prediction of missing values in a particular language, provided, the name of the language family, its genus, location (in terms of latitude and longitude coordinates and name of the country where it is spoken) and a set of feature-value pair are available. As part of fulfillment of the aforementioned task, the team submitted 3 kinds of system - 2 rule-based and one hybrid system. Of these 3, one rule-based system generated the best performance on the test set. All the systems were ‘constrained’ in the sense that no additional dataset or information, other than those provided by the organisers, was used for developing the systems.