The reviewed record of science sign in
Pith

arxiv: 2005.00085 · v1 · pith:R3ZPIC54 · submitted 2020-04-30 · cs.CL

AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages

Anoop Kunchukuttan , Divyanshu Kakwani , Satish Golla , Gokul N.C. , Avik Bhattacharyya , Mitesh M. Khapra , Pratyush Kumar This is my paper

Reviewed by Pithpith:R3ZPIC54open to challenge →

classification cs.CL
keywords corpusembeddingsindicnlplanguagesai4bharat-indicnlpavailablecorporaindic
0
0 comments X
read the original abstract

We present the IndicNLP corpus, a large-scale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families. We share pre-trained word embeddings trained on these corpora. We create news article category classification datasets for 9 languages to evaluate the embeddings. We show that the IndicNLP embeddings significantly outperform publicly available pre-trained embedding on multiple evaluation tasks. We hope that the availability of the corpus will accelerate Indic NLP research. The resources are available at https://github.com/ai4bharat-indicnlp/indicnlp_corpus.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Indi-RomCoM: Code-Mixed Benchmark for Evaluating LLMs on Romanized Indic-English Instructions

    cs.CL 2026-06 unverdicted novelty 7.0

    Introduces Indi-RomCoM benchmark for evaluating LLMs on Romanized code-mixed Indic-English instructions across seven tasks, four languages, and three mixing levels.

  2. Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics

    cs.CY 2026-04 unverdicted novelty 6.0

    Community members from the UK blind community, Kerala, and Tamil Nadu helped define what counts as culturally appropriate depictions of artifacts, and the authors tested whether those definitions can be turned into re...

  3. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    cs.CL 2022-11 unverdicted novelty 6.0

    BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.

  4. Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics

    cs.CY 2026-04 unverdicted novelty 5.0

    Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image mo...