Deep Learning for Biomedical Information Retrieval: Learning
            Textual Relevance from Click Logs

Mohan, Sanjay; Fiorini, Nicolas; Kim, Sun; Lu, Zhiyong

doi:10.18653/v1/w17-2328

Cited by 24 publications

(15 citation statements)

References 13 publications

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“…According to previous studies (Sunil et al 2017, Jun et al 2015), users' preferences and clicking behaviors show a strong semantic correlation. In this case, the dataset contains 200,000 pairwise training data, and we adopted a pairwise strategy.…”

Section: Stage 1: Deep Neural Network Model With Pairwise Learning Stmentioning

confidence: 76%

A Knowledge Based Two-Stage Cascade Model for Test-Question/Answer Retrieval

Wei¹,

Li²,

Madden³

2019

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; And Theory An

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Test-question/answer retrieval task has raised higher requirements in terms of accuracy, coverage and semantic understanding. We design a cascade model with two-stage training processes: The first stage uses 41,532 user test-question click records and 207,660 unclick records, which are collected from a designed test-question-answer experimental platform, to generate 200,000 pairwise training dataset to train a deep learning model, which could improve generalization ability. The second stage combines the output of the first stage with structural knowledge as new features to train a logistic regression for selecting the results from the candidates with higher accuracy, the training dataset is generated by manually annotating 20,000 test-question samples. The structural knowledge is also manually extracted from the samples for generating a small knowledge graph, and on this condition, we design knowledge features. Experimental results show that the proposed model outperforms the state-of-the-art algorithms, among which the cascading model contributes 3% improvement and the knowledge features contribute 1% improvement.

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Section: Stage 1: Deep Neural Network Model With Pairwise Learning Stmentioning

confidence: 76%

A Knowledge Based Two-Stage Cascade Model for Test-Question/Answer Retrieval

Wei¹,

Li²,

Madden³

2019

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; And Theory An

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show abstract

“…In addition to making more content available to users, the availability of the full text (including the text in figures and tables) could lead to improved search if we can develop automated methods for coping with the increased complexity associated with full texts ( Cohen et al, 2010 ). We are also studying the feasibility of using deep-learning technology ( Mohan et al, 2017 ; Severyn and Moschitti, 2015 ; Mikolov et al, 2013 ) in large-scale text mining applications.…”

Section: Discussionmentioning

confidence: 99%

Towards PubMed 2.0

Fiorini

Lipman

2017

eLife

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“…In the last couple of years, the work in biomedical NLP was dominated by applications of deep learning to: punctuation restoration [68], text classification [69], relation extraction [70] [71] [72] [73], information retrieval [74], and similarity judgments [75], among other exciting progress in biomedical language processing. For a more detailed exploration of recent topics, the BioNLP Annual Workshop [76] covers the most researched and debatable areas.…”

Section: Future Workmentioning

confidence: 99%

Biomedical Term Extraction: NLP Techniques in Computational Medicine

Sandoval¹,

Díaz²,

Llanos³

et al. 2019

IJIMAI

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Artificial Intelligence (AI) and its branch Natural Language Processing (NLP) in particular are main contributors to recent advances in classifying documentation and extracting information from assorted fields, Medicine being one that has gathered a lot of attention due to the amount of information generated in public professional journals and other means of communication within the medical profession. The typical information extraction task from technical texts is performed via an automatic term recognition extractor. Automatic Term Recognition (ATR) from technical texts is applied for the identification of key concepts for information retrieval and, secondarily, for machine translation. Term recognition depends on the subject domain and the lexical patterns of a given language, in our case, Spanish, Arabic and Japanese. In this article, we present the methods and techniques for creating a biomedical corpus of validated terms, with several tools for optimal exploitation of the information therewith contained in said corpus. This paper also shows how these techniques and tools have been used in a prototype.

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Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs

Cited by 24 publications

References 13 publications

A Knowledge Based Two-Stage Cascade Model for Test-Question/Answer Retrieval

A Knowledge Based Two-Stage Cascade Model for Test-Question/Answer Retrieval

Towards PubMed 2.0

Biomedical Term Extraction: NLP Techniques in Computational Medicine

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