Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications

TL;DR

This paper compiles and analyzes linguistic resources for Bhojpuri, Magahi, and Maithili, comparing their statistical and linguistic properties with Hindi, and establishing baselines for NLP tasks in these low-resource languages.

Contribution

It provides the first comprehensive statistical and annotated linguistic corpora for Bhojpuri, Magahi, and Maithili, along with comparison to Hindi and baseline results for NLP applications.

Findings

Corpora exhibit diverse morphological, lexical, phonological, and syntactic properties.

POS tagging and chunking datasets are established for each language.

Comparative analysis reveals linguistic similarities and differences among the languages.

Abstract

Corpus preparation for low-resource languages and for development of human language technology to analyze or computationally process them is a laborious task, primarily due to the unavailability of expert linguists who are native speakers of these languages and also due to the time and resources required. Bhojpuri, Magahi, and Maithili, languages of the Purvanchal region of India (in the north-eastern parts), are low-resource languages belonging to the Indo-Aryan (or Indic) family. They are closely related to Hindi, which is a relatively high-resource language, which is why we compare with Hindi. We collected corpora for these three languages from various sources and cleaned them to the extent possible, without changing the data in them. The text belongs to different domains and genres. We calculated some basic statistical measures for these corpora at character, word, syllable, and morpheme levels. These corpora were also annotated with parts-of-speech (POS) and chunk tags. The basic statistical measures were both absolute and relative and were exptected to indicate of linguistic properties such as morphological, lexical, phonological, and syntactic complexities (or richness). The results were compared with a standard Hindi corpus. For most of the measures, we tried to the corpus size the same across the languages to avoid the effect of corpus size, but in some cases it turned out that using the full corpus was better, even if sizes were very different. Although the results are not very clear, we try to draw some conclusions about the languages and the corpora. For POS tagging and chunking, the BIS tagset was used to manually annotate the data. The POS tagged data sizes are 16067, 14669 and 12310 sentences, respectively, for Bhojpuri, Magahi and Maithili. The sizes for chunking are 9695 and 1954 sentences for Bhojpuri and Maithili, respectively.