Using conditional random fields for result identification in biomedical abstracts

Lin, Ryan T.K.; Dai, Hong-Jie; Bow, Yue-Yang; Chiu, Justing Lian-Te; Tsai, Richard Tzong-Han

doi:10.3233/ica-2009-0321

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…The workload dedicated to reading all the articles selected by a classic search becomes so important that new approaches are needed. Several approaches are based on text-mining using machine learning methods [7][8][9] . However, they need to be trained on a carefully…”

Section: Discussionmentioning

confidence: 99%

“…These methods are very effective when the research question is clearly formulated, however, they afford little guidance when the informational need of the researcher is less circumscribed, for instance looking for risk factors or predictive factors. There are also many methods using machine learning algorithms [7][8][9] , or methods focused on specific topics 10 .…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

Bonnet

Jean-Pierre

Landais

2020

Preprint

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Background: Literature search is challenging when thousands of articles are potentially involved. To facilitate literature search we created TEMAS a Text Mining Algorithm-assisted Search tool that we compared to a PubMed reference search (RS) in the context of etiological epidemiology.Methods: The 4 steps of TEMAS are: 1) a classic PubMed global search 2) a first sort removing articles without abstracts or containing off-topic terms 3) a clustering step with a descending hierarchical classification regrouping articles in independent classes 4) a final sort extracting from the targeted class the abstracts containing the terms of interest, with a link to the corresponding PubMed articles. Validation was performed for risk factors of breast cancer. We estimated the precision and recall rate compared to RS. Average precision and discounted cumulative gain (DCG) were also computed to perform a ranking-based evaluation. We also compared TEMAS results with articles selected in two meta-analyses.Results: For risk factors of breast cancer, breastfeeding, mammographic density, oral contraceptive, and menarche were explored. TEMAS consistently increased precision vs RS (from 23% to 32%), with a recall rate from 95% to 97%, and divided the number of selected articles to read from 2.3 to 4.8 times. Mean average precision for 100 articles was 47.4% for TEMAS vs 20.9% for PubMed ranked by best match, and DCG showed a consistent improvement for TEMAS compared to PubMed best match.Discussion: TEMAS divided the results of a literature search by 3.2, and improved the precision rate, the average precision, and the DCG compared to RS for epidemiological studies. Reducing the number of selected articles inevitably impacted the recall rate. However, it remained satisfactory and did not bias the corpus of information. Moreover, the recall rate was 100% for the two meta-analyses we analyzed, which suggests that the loss of recall rate observed above concerned articles not relevant enough to be included in the meta-analyses.Conclusion: TEMAS provides a user-friendly interface for non-specialists of literature search confronted with thousands of articles and appeared useful for meta-analyses.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

Bonnet

Jean-Pierre

Landais

2020

Preprint

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“…All recognized relations would be clustered based on a control vocabulary such as the GENIA Event Ontology ( 16 ) for generalizing the integrated networks to improve ease of comprehension. We also intend to implement an automatic test system for the assessment of curator qualifications for the MET curation task, and integrate the result section recognizer ( 17 ) in our text mining service to focus on sentences in the ‘Results and Conclusion’ section of an abstract, thus mitigating the risk of extracting possibly misleading relations from the ‘Background or Method’ sections of the abstract.…”

Section: Discussionmentioning

confidence: 99%

MET network in PubMed: a text-mined network visualization and curation system

Dai

Lai

et al. 2016

Database

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Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET.Database URL: http://btm.tmu.edu.tw/metastasisway

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“…Linear text segmentation is an essential step in many natural language processing tasks, including information retrieval [33] and document summarization [22]. In information retrieval, a text segmentation program is required when a document related to the user's needs is too long for the user to explore.…”

Section: Introductionmentioning

confidence: 99%

A hybrid linear text segmentation algorithm using hierarchical agglomerative clustering and discrete particle swarm optimization

Tseng

Tsai

2014

ICA

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Linear text segmentation plays an important role in many natural language processing tasks. Many algorithms have been proposed and shown to improve the performance of linear text segmentation. However, the previous studies often suffer from either lower segmentation accuracy or higher computational complexity. Moreover, parameter setting is another critical problem in some algorithms. Although manual assignment is an approach to solve this problem, it may increase the user's burden, and the parameters provided may not always be suitable to reflect the real metadata of a text. In this paper, a hybrid algorithm, TSHAC-DPSO, is proposed to tackle these problems. A novel linear Text Segmentation algorithm based on Hierarchical Agglomerative Clustering (TSHAC) is proposed to rapidly generate a satisfactory solution without an auxiliary knowledge base, parameter setting, or user involvement; then an efficient evolutional algorithm, Discrete Particle Swarm Optimization (DPSO), is adopted to generate the global optimal solution by refining the solution created by TSHAC. TSHAC-DPSO fully utilizes the merits of both algorithms which not only improve the accuracy of linear text segmentation, but also make the execution more efficient and flexible. The experimental results show that TSHAC-DPSO provides comparable segmentation accuracy with several well-known linear text segmentation algorithms.

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Using conditional random fields for result identification in biomedical abstracts

Cited by 10 publications

References 35 publications

Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

MET network in PubMed: a text-mined network visualization and curation system

A hybrid linear text segmentation algorithm using hierarchical agglomerative clustering and discrete particle swarm optimization

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