Unsupervised discovery of information structure in biomedical documents

Guo, Yufan; Stenius, Ulla; Korhonen, Anna

doi:10.1093/bioinformatics/btu758

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…In Guo et al (2015), clustering methods have been improved to identify the zones by extracting the latent topics from the articles and the sections. Moreover, in the special case of the abstracts of biology papers, Kiela et al (2014) used clustering approaches such as k-means and multi-level-weighted graph to obtain a high performance in zone identification; This is even more evident in zones like Objective, Result and Conclusion.…”

Section: Related Workmentioning

confidence: 99%

Automatic zone identification in scientific papers via fusion techniques

Asadi

Badie

Mahmoudi³

2019

Scientometrics

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Zone identification is a topic in the area of text mining which helps researchers be benefited by the content of scientific papers in a satisfactory manner. The major aim of zone identification is to classify the sentences of scientific texts into some predefined zone categories which can be useful for summarization as well as information extraction. In this paper, we propose a two-level approach to zone identification within which the first level is in charge of classifying the sentences in a given paper based on some semantic and lexical features. In this respect, several machine learning algorithms such as Simple Logistics, Logistic Model Trees and Sequential Minimal Optimization are applied. The second level is responsible for applying fusion to the classification results obtained for consecutive sentences of the first level in order to make the final decision. The proposed method is evaluated on ART and DRI corpora as two well-known data sets. Results obtained for the accuracy of zone identification for these corpora are respectively 65.75% and 84.15%, which seem to be quite promising compared to those obtained by previous approaches.

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Section: Related Workmentioning

confidence: 99%

Automatic zone identification in scientific papers via fusion techniques

Asadi

Badie

Mahmoudi³

2019

Scientometrics

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“…Most studies in this area focus on improving automatic discourse parsing of scientific text, while some works also focus on the linguistic patterns and psychological effects of scientific argumentation (e.g., de Waard and Maat, 2012). A wide range of techniques have been used in prior work to parse scientific abstracts, from fully supervised techniques (Chung, 2009;Guo et al, 2010) to semi-supervised (Guo et al, 2011c;Guo et al, 2013) and unsupervised techniques (Kiela et al, 2015).…”

Section: Scientific Discourse Analysismentioning

confidence: 99%

Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing

Prabhakaran¹,

Hamilton²,

McFarland³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Computationally modeling the evolution of science by tracking how scientific topics rise and fall over time has important implications for research funding and public policy. However, little is known about the mechanisms underlying topic growth and decline. We investigate the role of rhetorical framing: whether the rhetorical role or function that authors ascribe to topics (as methods, as goals, as results, etc.) relates to the historical trajectory of the topics. We train topic models and a rhetorical function classifier to map topic models onto their rhetorical roles in 2.4 million abstracts from the Web of Science from 1991-2010. We find that a topic's rhetorical function is highly predictive of its eventual growth or decline. For example, topics that are rhetorically described as results tend to be in decline, while topics that function as methods tend to be in early phases of growth.

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“…Most previous work on automatic analysis of information structure relies on supervised learning (Teufel and Moens, 2002;Burstein et al, 2003;Mizuta et al, 2006;Shatkay et al, 2008;Guo et al, 2010;Liakata et al, 2012;Markert et al, 2012). Given the prohibitive cost of manual annotation, unsupervised and minimally supervised techniques such as clustering (Kiela et al, 2014) and topic modeling (Varga et al, 2012;O Séaghdha and Teufel, 2014) are highly important. However, the performance of such approaches shows a large room for improvement.…”

Section: Previous Workmentioning

confidence: 99%

Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents

Guo

Reichart

Korhonen

2015

TACL

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Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised feature-based models as well as unsupervised models that use manually constructed declarative knowledge. Our results demonstrate that useful declarative knowledge can be learned from data with very limited human involvement.

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Unsupervised discovery of information structure in biomedical documents

Abstract: The annotated corpus and software are available at http://www.cl.cam.ac.uk/∼dk427/bio14info.html.

Cited by 6 publications

References 22 publications

Automatic zone identification in scientific papers via fusion techniques

Automatic zone identification in scientific papers via fusion techniques

Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing

Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents

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