Ken Chen


2024

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KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction – Subtask2
Yukun Tan | Merve Dede | Ken Chen
Proceedings of the 6th Clinical Natural Language Processing Workshop

This paper presents our participation in the Chemotimelines 2024 subtask2, focusing on the development of an end-to-end system for chemotherapy timeline extraction. We initially adopt a basic framework from subtask2, utilizing Apache cTAKES for entity recognition and a BERT-based model for classifying the temporal relationship between chemotherapy events and associated times. Subsequently, we enhance this pipeline through two key directions: first, by expanding the exploration of the system, achieved by extending the search dictionary of cTAKES with the UMLS database; second, by reducing false positives through preprocessing of clinical notes and implementing filters to reduce the potential errors from the BERT-based model. To validate the effectiveness of our framework, we conduct extensive experiments using clinical notes from breast, ovarian, and melanoma cancer cases. Our results demonstrate improvements over the previous approach.

2018

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Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
Zhenghui Wang | Yanru Qu | Liheng Chen | Jian Shen | Weinan Zhang | Shaodian Zhang | Yimei Gao | Gen Gu | Ken Chen | Yong Yu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.

2004

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Speech Recognition Models of the Interdependence Among Syntax, Prosody, and Segmental Acoustics
Mark Hasegawa-Johnson | Jennifer Cole | Chilin Shih | Ken Chen | Aaron Cohen | Sandra Chavarria | Heejin Kim | Taejin Yoon | Sarah Borys | Jeung-Yoon Choi
Proceedings of the HLT-NAACL 2004 Workshop on Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing