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Intent Detection and Entity Extraction from BioMedical Literature (2404.03598v2)

Published 4 Apr 2024 in cs.CL

Abstract: Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature. Despite recent research on large-LLMs motivated by endeavours to attain generalized intelligence, their efficacy in replacing task and domain-specific natural language understanding approaches remains questionable. In this paper, we address this question by conducting a comprehensive empirical evaluation of intent detection and named entity recognition (NER) tasks from biomedical text. We show that Supervised Fine Tuned approaches are still relevant and more effective than general-purpose LLMs. Biomedical transformer models such as PubMedBERT can surpass ChatGPT on NER task with only 5 supervised examples.

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References (52)
  1. Language models are few-shot learners.
  2. A benchmark dataset and case study for chinese medical question intent classification. BMC Medical Informatics and Decision Making, 20.
  3. Palm: Scaling language modeling with pathways.
  4. Nigel Collier and Jin-Dong Kim. 2004. Introduction to the bio-entity recognition task at JNLPBA. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA/BioNLP), pages 73–78, Geneva, Switzerland. COLING.
  5. Bert: Pre-training of deep bidirectional transformers for language understanding.
  6. Alpacafarm: A simulation framework for methods that learn from human feedback.
  7. Gpts are gpts: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
  8. Dynamic gazetteer integration in multilingual models for cross-lingual and cross-domain named entity recognition. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2777–2790, Seattle, United States. Association for Computational Linguistics.
  9. Exploiting and assessing multi-source data for supervised biomedical named entity recognition. Bioinformatics, 34(14):2474–2482.
  10. John M Giorgi and Gary D Bader. 2019. Towards reliable named entity recognition in the biomedical domain. Bioinformatics, 36(1):280–286.
  11. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1):1–23.
  12. Matscie: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature. Computational Materials Science (Comput. Mater. Sci.), 192:110325.
  13. Training compute-optimal large language models.
  14. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
  15. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240.
  16. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford), 2016:baw068.
  17. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  18. Classification under streaming emerging new classes: A solution using completely-random trees. IEEE Transactions on Knowledge and Data Engineering, 29(8):1605–1618.
  19. Streaming classification with emerging new class by class matrix sketching. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31.
  20. Ankan Mullick. 2023a. Exploring multilingual intent dynamics and applications. IJCAI Doctoral Consortium.
  21. Ankan Mullick. 2023b. Novel intent detection and active learning based classification (student abstract). arXiv e-prints, pages arXiv–2304.
  22. Matscire: Leveraging pointer networks to automate entity and relation extraction for material science knowledge-base construction. Computational Materials Science, 233:112659.
  23. Identifying opinion and fact subcategories from the social web. In Proceedings of the 2018 ACM International Conference on Supporting Group Work, pages 145–149.
  24. A graphical framework to detect and categorize diverse opinions from online news. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 40–49.
  25. Extracting social lists from twitter. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pages 391–394.
  26. Harnessing twitter for answering opinion list queries. IEEE Transactions on Computational Social Systems, 5(4):1083–1095.
  27. A generic opinion-fact classifier with application in understanding opinionatedness in various news section. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 827–828.
  28. Intent identification and entity extraction for healthcare queries in indic languages. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1825–1836.
  29. Fine-grained intent classification in the legal domain. arXiv preprint arXiv:2205.03509.
  30. D-fj: Deep neural network based factuality judgment. Technology, 50:173.
  31. A framework to generate high-quality datapoints for multiple novel intent detection. arXiv preprint arXiv:2205.02005.
  32. Open-domain anatomical entity mention detection. In Proceedings of the Workshop on Detecting Structure in Scholarly Discourse, pages 27–36, Jeju Island, Korea. Association for Computational Linguistics.
  33. OpenAI. 2023. Gpt-4 technical report.
  34. Training language models to follow instructions with human feedback.
  35. Dipankar Sarkar and Mukur Gupta. 2021. Tso: Curriculum generation using continuous optimization. CoRR.
  36. SemEval-2013 task 9 : Extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013). In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 341–350, Atlanta, Georgia, USA. Association for Computational Linguistics.
  37. Overview of BioCreative II gene mention recognition. Genome Biol., 9 Suppl 2(S2):S2.
  38. Online ensemble learning of data streams with gradually evolved classes. IEEE Transactions on Knowledge and Data Engineering, 28(6):1532–1545.
  39. Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 142–147.
  40. Llama: Open and efficient foundation language models.
  41. Llama 2: Open foundation and fine-tuned chat models.
  42. Active learning through label error statistical methods. Knowledge-Based Systems, 189:105140.
  43. Gpt-ner: Named entity recognition via large language models.
  44. Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system. arXiv preprint arXiv:2104.11882.
  45. Zero-shot user intent detection via capsule neural networks. arXiv preprint arXiv:1809.00385.
  46. A better entity detection of question for knowledge graph question answering through extracting position-based patterns. Journal of Big Data, 9(1):1–26.
  47. One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era.
  48. Bringing semantic structures to user intent detection in online medical queries. In 2017 IEEE International Conference on Big Data (Big Data), pages 1019–1026. IEEE.
  49. Cblue: A chinese biomedical language understanding evaluation benchmark.
  50. Optimizing bi-encoder for named entity recognition via contrastive learning.
  51. A bert based sentiment analysis and key entity detection approach for online financial texts. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 1233–1238. IEEE.
  52. Natural language processing for smart healthcare. arXiv preprint arXiv:2110.15803.
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Authors (3)
  1. Ankan Mullick (18 papers)
  2. Mukur Gupta (6 papers)
  3. Pawan Goyal (170 papers)
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