Leveraging coreference to identify arms in medical abstracts: An
            experimental study

Ferracane, Elisa; Marshall, Iain J.; Wallace, Byron C.; Erk, Katrin

doi:10.18653/v1/w16-6112

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Prior work has explored the use of NLP methods to automate biomedical evidence extraction and synthesis (Boudin et al, 2010; Marshall et al, 2017; Ferracane et al, 2016; Verbeke et al, 2012). 1 But the area has attracted less attention than it might from the NLP community, due primarily to a dearth of publicly available, annotated corpora with which to train and evaluate models.…”

Section: Introductionmentioning

confidence: 99%

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Nye¹,

Li²,

Patel³

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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155

207

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We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the ‘PICO’ elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.

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Section: Introductionmentioning

confidence: 99%

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Nye¹,

Li²,

Patel³

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Self Cite

155

207

View full text Add to dashboard Cite

show abstract

“…Event coreference resolution is the task of determining which event mentions expressed in language refer to the same real-world event instances. The ability to resolve event coreference has improved the quality of downstream tasks such as automatic text summarization (Vanderwende et al, 2004), questioning-answering (Berant et al, 2014), headline generation (Sun et al, 2015), and text-mining in the medical domain (Ferracane et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization

Kenyon-Dean

Cheung

Precup

2018

Proceedings of the Seventh Joint Conference on Lexical And Computational Semantics

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We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both withinand cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more preannotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.

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“…Brujin et al (De Bruijn et al, 2008) combined an SVM-based text classifier with regular expressions to extract PICO elements. Further, Ferracane et al (Ferracane et al, 2016) aim to leverage co-reference resolution to identify experimental groups (patients) from medical abstracts. However, none of these works aims at deeper extraction of arms/experimental groups and their properties.…”

Section: Related Workmentioning

confidence: 99%

“…While there has been significant progress on information extraction tasks with a comparably low level of structural complexity such as entity recognition (Goulart et al, 2011;Nadeau and Sekine, 2007), relation extraction (Zhou et al, 2014;Kumar, 2017), and co-reference resolution (Soon et al, 2001;Ferracane et al, 2016), there is not much progress on capturing the comprehensive meaning of a text with respect to a given semantic model in terms of a given vocabulary of classes and properties. We refer to this task as model-complete text comprehension (MCTC) which requires to put all the above mentioned classical NLP-tasks into a larger context.…”

Section: Introductionmentioning

confidence: 99%

Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension

Horst

Cimiano

2020

Proceedings of the Fourth Workshop on Structured Prediction for NLP

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Model-complete text comprehension aims at interpreting a natural language text with respect to a semantic domain model describing the classes and their properties relevant for the domain in question. Solving this task can be approached as a structured prediction problem, consisting in inferring the most probable instance of the semantic model given the text. In this work, we focus on the challenging subproblem of cardinality prediction that consists in predicting the number of distinct individuals of each class in the semantic model. We show that cardinality prediction can successfully be approached by modeling the overall task as a joint inference problem, predicting the number of individuals of certain classes while at the same time extracting their properties. We approach this task with probabilistic graphical models computing the maximum-aposteriori instance of the semantic model. Our main contribution lies on the empirical investigation and analysis of different approximative inference strategies based on Gibbs sampling. We present and evaluate our models on the task of extracting key parameters from scientific full text articles describing pre-clinical studies in the domain of spinal cord injury.

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Leveraging coreference to identify arms in medical abstracts: An experimental study

Cited by 8 publications

References 28 publications

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization

Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension

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