The Computational Language Understanding (CLU) Lab is principally concerned with advancing computational models to understand the semantics of human language and translating these models into clinical practice. Our approach is to seek a good understanding of human language, to capture the essential features of language in appropriate models, and to develop the necessary computational framework to advance knowledge and discovery in Natural Language Processing (NLP) and Healthcare. We accomplish this through rigorous computational research coupled with partnerships with linguists and clinicians.
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Members

Hadi Amiri Assistant Professor

Mohamed Elgaar PhD student, F'20

Nidhi Vakil PhD student, F'20

Jiali Cheng PhD student, S'23
Research
Several specific areas that we are currently investigating are listed below:
Curriculum Learning for NLP: Deep neural networks can effectively tackle many NLP tasks, but they could be computationally expensive to train. How can we uncover the salient characteristics of these learners (networks) and their learning materials (training data) for effective representation and efficient training?
Clinical Decision Support: Medical information in referral letters, physician notes or scientific articles are locked in unstructured text. What are the best techniques to extract insight from such data, represent patient data and reference materials about diseases, triage patient applications, pinpoint disease-causing gene variants, and enhance clinical decision support systems with evidence?
Social Media Surveillance: User generated content in social media present naturally occurring data that can be used to obtain low-cost and high-resolution views into population behavior. How can we develop online surveillance systems that can monitor population behavior at scale to detect (health-related) trends and outbreaks, and identify opportunities for decision making or intervention?
See further details here.
Recent Publications
MedDecXtract: A Clinician-Support System for Extracting, Visualizing, and Annotating Medical Decisions in Clinical Narratives
Mohamed Elgaar, Hadi Amiri. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics Systems Demonstration (ACL’25 Demo) [Demo]
Speech Unlearning
Jiali Cheng, Hadi Amiri. In Proceedings of INTERSPEECH 2025 (INTERSPEECH’25) [PDF]
Tool Unlearning for Tool-Augmented LLMs
Jiali Cheng, Hadi Amiri. In Proceedings of International Conference on Machine Learning (ICML’25) [PDF]
EqualizeIR: Mitigating Linguistic Biases in Retrieval Models
Jiali Cheng, Hadi Amiri. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL’25) [PDF]
Controlled Transformation of Text-Attributed Graphs
Nidhi Vakil, Hadi Amiri. In Proceedings of The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP’24) (Findings). [PDF]
FairFlow: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding
Jiali Cheng, Hadi Amiri. In Proceedings of The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP’24). [PDF]
MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, Hadi Amiri, Leo Anthony Celi. In Findings of the Association for Computational Linguistics (ACL’24) (Findings). [PDF]
CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech
Jiali Cheng, Mohamed Elgaar, Nidhi Vakil, Hadi Amiri. In Proceedings of INTERSPEECH 2024 (INTERSPEECH’24). [PDF]
MultiDelete for Multimodal Machine Unlearning
Jiali Cheng, Hadi Amiri. In Proceedings of The 18th European Conference on Computer Vision (ECCV’24). [PDF]
See the complete list here.
Contact
Address: One University Avenue, 320 Southwick Hall, Lowell, MA 01854
Email: clu@cs.uml.edu