# Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

arXiv: https://arxiv.org/abs/2302.09685 · HTML: https://mlsanity.com/paper/2302.09685/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2106.08087/) (2021)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)
- [Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System](https://mlsanity.com/paper/2104.11882/) (2021)
- [A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts](https://mlsanity.com/paper/2001.05326/) (2020)
- [Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities](https://mlsanity.com/paper/1912.03457/) (2019)
- [Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees](https://mlsanity.com/paper/1605.09131/) (2016)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU](https://mlsanity.com/paper/1910.00912/) (2019)
- [Fine-grained Intent Classification in the Legal Domain](https://mlsanity.com/paper/2205.03509/) (2022)
- [Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher](https://mlsanity.com/paper/2010.02562/) (2020)
- [HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention](https://mlsanity.com/paper/2002.03140/) (2020)
- [Bringing Semantic Structures to User Intent Detection in Online Medical Queries](https://mlsanity.com/paper/1710.08015/) (2017)
- [Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi](https://mlsanity.com/paper/2004.10270/) (2020)
- [TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting](https://mlsanity.com/paper/2102.00509/) (2021)
- [Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic?](https://mlsanity.com/paper/2204.02790/) (2022)
- [Effective Transfer Learning for Low-Resource Natural Language Understanding](https://mlsanity.com/paper/2208.09180/) (2022)

## Built on by (most cited first, 9 of 9)

- [Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT](https://mlsanity.com/paper/2404.09296/) (2024)
- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection](https://mlsanity.com/paper/2508.16122/) (2025)
- [Intent Detection and Entity Extraction from BioMedical Literature](https://mlsanity.com/paper/2404.03598/) (2024)
- [On The Persona-based Summarization of Domain-Specific Documents](https://mlsanity.com/paper/2406.03986/) (2024)
- [A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents](https://mlsanity.com/paper/2410.22476/) (2024)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)
- [Taxonomy of Comprehensive Safety for Clinical Agents](https://mlsanity.com/paper/2509.22041/) (2025)
- [IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction](https://mlsanity.com/paper/2511.05921/) (2025)

## Abstract (arXiv)

Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.