Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages

• URL: http://arxiv.org/abs/2405.04829v2
• Date: Fri, 10 May 2024 12:57:50 GMT
• Title: Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
• Authors: Sankalp Bahad, Pruthwik Mishra, Karunesh Arora, Rakesh Chandra Balabantaray, Dipti Misra Sharma, Parameswari Krishnamurthy,
• Abstract summary: We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian languages. We present a human annotated named entity corpora of 40K sentences for 4 Indian languages from two of the major Indian language families. We achieve comparable performance on completely unseen benchmark datasets for Indian languages which affirms the usability of our model.
• Score: 6.7638050195383075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answering systems. The research on NER is centered around English and some other major languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian Languages. We present a human annotated named entity corpora of 40K sentences for 4 Indian languages from two of the major Indian language families. Additionally,we present a multilingual model fine-tuned on our dataset, which achieves an F1 score of 0.80 on our dataset on average. We achieve comparable performance on completely unseen benchmark datasets for Indian languages which affirms the usability of our model.

Related papers

• Navigating Text-to-Image Generative Bias across Indic Languages [53.92640848303192]
  This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English.
  arXiv  Detail & Related papers  (2024-08-01T04:56:13Z)
• Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning [49.79783940841352]
  Existing datasets are almost all in the English language. We work with fluent speakers of languages from around the world to collect natural instances of instructions and completions. We create the most extensive multilingual collection to date, comprising 513 million instances through templating and translating existing datasets across 114 languages.
  arXiv  Detail & Related papers  (2024-02-09T18:51:49Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Enhancing Low Resource NER Using Assisting Language And Transfer Learning [0.7340017786387767]
  We use baseBERT, AlBERT, and RoBERTa to train a supervised NER model. We show that models trained using multiple languages perform better than a single language.
  arXiv  Detail & Related papers  (2023-06-10T16:31:04Z)
• PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India [33.31556860332746]
  PMIndiaSum is a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs.
  arXiv  Detail & Related papers  (2023-05-15T17:41:15Z)
• Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages [15.214673043019399]
  The dataset contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories. The training dataset has been automatically created from the Samanantar parallel corpus. We release IndicNER, a multilingual IndicBERT model fine-tuned on Naamapadam training set.
  arXiv  Detail & Related papers  (2022-12-20T11:15:24Z)
• MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition [55.95128479289923]
  African languages are spoken by over a billion people, but are underrepresented in NLP research and development. We create the largest human-annotated NER dataset for 20 African languages. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points.
  arXiv  Detail & Related papers  (2022-10-22T08:53:14Z)
• Challenge Dataset of Cognates and False Friend Pairs from Indian Languages [54.6340870873525]
  Cognates are present in multiple variants of the same text across different languages. In this paper, we describe the creation of two cognate datasets for twelve Indian languages.
  arXiv  Detail & Related papers  (2021-12-17T14:23:43Z)
• Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages [50.82410844837726]
  We demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian languages. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages. We observe an improvement of up to 18% points, in terms of F-score, for cognate detection.
  arXiv  Detail & Related papers  (2021-12-16T11:17:58Z)
• CL-NERIL: A Cross-Lingual Model for NER in Indian Languages [0.5926203312586108]
  This paper proposes an end-to-end framework for NER for Indian languages. We exploit parallel corpora of English and Indian languages and an English NER dataset. We present manually annotated test sets for three Indian languages: Hindi, Bengali, and Gujarati.
  arXiv  Detail & Related papers  (2021-11-23T12:09:15Z)
• Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages [0.8155575318208631]
  Language models based on the Transformer architecture have achieved state-of-the-art performance on a wide range of NLP tasks. However, this performance is usually tested and reported on high-resource languages, like English, French, Spanish, and German. Indian languages, on the other hand, are underrepresented in such benchmarks.
  arXiv  Detail & Related papers  (2020-11-04T14:43:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.