Chen Lin


2020

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Extracting Relations between Radiotherapy Treatment Details
Danielle Bitterman | Timothy Miller | David Harris | Chen Lin | Sean Finan | Jeremy Warner | Raymond Mak | Guergana Savova
Proceedings of the 3rd Clinical Natural Language Processing Workshop

We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.

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Defining and Learning Refined Temporal Relations in the Clinical Narrative
Kristin Wright-Bettner | Chen Lin | Timothy Miller | Steven Bethard | Dmitriy Dligach | Martha Palmer | James H. Martin | Guergana Savova
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.

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A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Farig Sadeque | Steven Bethard | Guergana Savova
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through each input sequence. We extend a recently-proposed one-pass model for relation classification to a one-pass model for relation extraction. We augment this framework by introducing global embeddings to help with long-distance relation inference, and by multi-task learning to increase model performance and generalizability. Our proposed model produces results on par with the state-of-the-art in temporal relation extraction on the THYME corpus and is much “greener” in computational cost.

2019

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A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
Proceedings of the 2nd Clinical Natural Language Processing Workshop

Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.

2018

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Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Hadi Amiri | Steven Bethard | Guergana Savova
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance. We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework. Our framework makes use of labeled, unlabeled, and social media data, operates on basic features, and is scalable and generalizable. With this method, we establish the state-of-the-art result for both in- and cross-domain for a clinical temporal relation extraction task.

2017

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Neural Temporal Relation Extraction
Dmitriy Dligach | Timothy Miller | Chen Lin | Steven Bethard | Guergana Savova
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.

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Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
BioNLP 2017

Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.

2016

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Improving Temporal Relation Extraction with Training Instance Augmentation
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

2015

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Extracting Time Expressions from Clinical Text
Timothy Miller | Steven Bethard | Dmitriy Dligach | Chen Lin | Guergana Savova
Proceedings of BioNLP 15

2014

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Descending-Path Convolution Kernel for Syntactic Structures
Chen Lin | Timothy Miller | Alvin Kho | Steven Bethard | Dmitriy Dligach | Sameer Pradhan | Guergana Savova
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Temporal Annotation in the Clinical Domain
William F. Styler IV | Steven Bethard | Sean Finan | Martha Palmer | Sameer Pradhan | Piet C de Groen | Brad Erickson | Timothy Miller | Chen Lin | Guergana Savova | James Pustejovsky
Transactions of the Association for Computational Linguistics, Volume 2

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

2013

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Discovering Temporal Narrative Containers in Clinical Text
Timothy Miller | Steven Bethard | Dmitriy Dligach | Sameer Pradhan | Chen Lin | Guergana Savova
Proceedings of the 2013 Workshop on Biomedical Natural Language Processing