Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
Copied to clipboard
Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families. |
| Outcome: | The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. |
Similar Papers
HiNER: A large Hindi Named Entity Recognition Dataset (2022.lrec-1)
Copied to clipboard
Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
| Approach: | They propose to use a standard-abiding Hindi NER dataset to analyze the annotations of a class of naming entities in free text. |
| Outcome: | The proposed dataset achieves a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set. |
AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition (2022.lrec-1)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a type of annotation that classifies text into predefined classes such as person, location, organization etc. |
| Approach: | They propose to use a named entity annotation dataset for low resource Assamese language with a baseline NER model. |
| Outcome: | The proposed dataset is likely to be significant resource for deep neural based Assamese language processing. |
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages (2024.naacl-srw)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a useful component in NLP applications. |
| Approach: | They propose to use annotated named entity corpora to classify a given entity into a category within a textual document. |
| Outcome: | The proposed model achieves an F1 score of 0.80 on an unseen dataset for Indian languages. |
OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets are not consistently formatted and use a variety of chunk encodings (IOB, BIO, etc.), often without documentation. |
| Approach: | They present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets. |
| Outcome: | The proposed datasets correct annotation format issues and provide a structure that enables research in multilingual and multi-ontology NER. |
Thai Nested Named Entity Recognition Corpus (2022.findings-acl)
Copied to clipboard
Weerayut Buaphet, Can Udomcharoenchaikit, Peerat Limkonchotiwat, Attapol Rutherford, Sarana Nutanong
| Challenge: | a new dataset for Named Entity Recognition (NER) is proposed for Thailand. |
| Approach: | They propose to use Thai N-NER to extract named entities from text . they propose to include a nested structure that can be used to improve NER . |
| Outcome: | The proposed dataset is the largest non-English N-NER dataset and the first non- English one with fine-grained classes. |
MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
| Approach: | They present a large multilingual dataset for Named Entity Recognition that covers 3 domains across 11 languages and multilingual and code-mixing subsets. |
| Outcome: | The proposed dataset is large and multilingual, covering 11 languages and subsets. |
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
| Outcome: | The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set. |
BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset (L18-1)
Copied to clipboard
| Challenge: | Named-entity recognition (NER) is a natural language processing component that aims to identify all the "named entities" (NEs) in an unstructured text. |
| Approach: | They propose a deep learning approach for name-entity recognition in Persian . they publicize an entity-annotated Persian dataset and train word embeddings . |
| Outcome: | The proposed approach achieves a 77.45% CoNLL F 1 score for Persian NER based on a deep learning architecture and pre-trained word embeddings. |
Towards a Standardized Dataset on Indonesian Named Entity Recognition (2020.aacl-srw)
Copied to clipboard
| Challenge: | Named entity recognition (NER) tasks in the Indonesian language are still lacking data for the majority of languages, including Indonesian. |
| Approach: | They re-annotated an open dataset with 2,000 sentences and compared the results with a bidirectional long short-term memory and conditional random field approach. |
| Outcome: | The proposed approach improved the prediction score and consistent organization tag for the Indonesian language. |
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. |
| Approach: | They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER . |
| Outcome: | The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages. |