Automatic Detection of Answers to Research Questions from Medline Abstracts

Stevenson, Mark

doi:10.18653/v1/w15-3817

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…The ACRES system (Summerscales et al, 2011) produces summaries of several trial characteristic, and was trained on 263 annotated abstracts. Hinting at more challenging tasks that can build upon foundational information extraction, Alamri and Stevenson (2015) developed methods for detecting contradictory claims in biomedical papers. Their corpus of annotated claims contains 259 sentences (Alamri and Stevenson, 2016).…”

Section: Related Workmentioning

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)

154

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: Related Workmentioning

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)

154

207

View full text Add to dashboard Cite

show abstract

“…Table 1 shows the performance results of the claim selection component. The authors in [1] relied on lexical similarity and a Z-score that computes the sentence relevance, with respect to the distribution of similarity scores of other sentences across the dataset. However, While this scoring function contributes to precision, it also affects the recall performance metric.…”

Section: Claim Extraction Resultsmentioning

confidence: 99%

Automated Contradiction Detection in Biomedical Literature

Tawfik

Spruit

2018

Machine Learning and Data Mining in Pattern Recognition

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Medical literature suffers from inconsistencies between reported findings that answer the same research question. This paper introduces an automated two-phase contradiction detection model that integrates semantic properties as input features to a Learning-to-Rank framework, to accurately identify key findings of a research article. It also relies on negation, antonyms and similarity measures to detect contradictions between findings. The proposed technique is implemented and tested on a publicly available contradiction corpus 259 manually annotated abstracts. The performance is compared based on recall, precision and F-measure. Experimental evaluations prove the utility of the model and its contribution to the contradiction classification and extraction task.

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“…The work by Shi and Bei (2019) is one of the few exceptions that target this challenge and propose a pipeline to extract health-related claims from headlines of healththemed news articles. The majority of other argument mining approaches for the biomedical domain focus on research literature (Blake, 2010;Alamri and Stevenson, 2015;Alamri and Stevensony, 2015;Achakulvisut et al, 2019;Mayer et al, 2020).…”

Section: Claim Detectionmentioning

confidence: 99%

Claim Detection in Biomedical Twitter Posts

Wührl¹,

Klinger²

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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Social media contains unfiltered and unique information, which is potentially of great value, but, in the case of misinformation, can also do great harm. With regards to biomedical topics, false information can be particularly dangerous. Methods of automatic fact-checking and fake news detection address this problem, but have not been applied to the biomedical domain in social media yet. We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase). With this corpus, which we sample to be related to COVID-19, measles, cystic fibrosis, and depression, we develop baseline models which detect tweets that contain a claim automatically. Our analyses reveal that biomedical tweets are densely populated with claims (45 % in a corpus sampled to contain 1200 tweets focused on the domains mentioned above). Baseline classification experiments with embedding-based classifiers and BERT-based transfer learning demonstrate that the detection is challenging, however, shows acceptable performance for the identification of explicit expressions of claims. Implicit claim tweets are more challenging to detect.

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Automatic Detection of Answers to Research Questions from Medline Abstracts

Cited by 10 publications

References 14 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

Automated Contradiction Detection in Biomedical Literature

Claim Detection in Biomedical Twitter Posts

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