Topic detection using paragraph vectors to support active learning in systematic reviews

Journal of Biomedical Informatics

Volumn 62, Issue , 2016, Pages 59-65

  Hashimoto, Kazuma ^a   Kontonatsios, Georgios ^b   Miwa, Makoto ^c   Ananiadou, Sophia ^b  

^a UNIVERSITY OF TOKYO   (Japan)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

^c TOYOTA TECHNOLOGICAL INSTITUTE   (Japan)

Author keywords

Active learning; Citation screening; Document embeddings; Paragraph vectors; Systematic reviews; Topic modelling

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); SEMANTICS; STATISTICS; SUPERVISED LEARNING; TEXT PROCESSING; VECTOR SPACES; VECTORS;

ACTIVE LEARNING; ACTIVE LEARNING STRATEGIES; EMBEDDINGS; LATENT DIRICHLET ALLOCATION; SEMANTIC SIMILARITY; SUPERVISED MACHINE LEARNING; SYSTEMATIC REVIEW; VECTOR SPACE MODELS;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CONTROLLED STUDY; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; SYSTEMATIC REVIEW (TOPIC); VALIDITY; CLASSIFICATION; HUMAN; LITERATURE; SEMANTICS;

CLASSIFICATION; HUMANS; MACHINE LEARNING; REVIEW LITERATURE AS TOPIC; SEMANTICS; SUPPORT VECTOR MACHINE;

EID: 84974733372     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2016.06.001     Document Type: Article

References (26)

1. Gough D., Oliver S., Thomas J. An Introduction to Systematic Reviews 2012, Sage.
2. Chalmers I., Hedges L.V., Cooper H. A brief history of research synthesis. Eval. Health Prof. 2002, 25(1):12-37.
3. O'Mara-Eves A., Thomas J., McNaught J., Miwa M., Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst. Rev. 2015, 4(1):5.
4. Miwa M., Thomas J., O'Mara-Eves A., Ananiadou S. Reducing systematic review workload through certainty-based screening. J. Biomed. Inform. 2014, 51:242-253.
5. Wallace B.C., Trikalinos T.A., Lau J., Brodley C., Schmid C.H. Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinform. 2010, 11(1):55.
6. Cohen A.M., Hersh W.R., Peterson K., Yen P.-Y. Reducing workload in systematic review preparation using automated citation classification. J. Am. Med. Inform. Assoc. 2006, 13(2):206-219.
7. Beahler C.C., Sundheim J.J., Trapp N.I. Information retrieval in systematic reviews: challenges in the public health arena. Am. J. Prev. Med. 2000, 18(4):6-10.
8. Blei D.M., Ng A.Y., Jordan M.I. Latent Dirichlet allocation. J. Mach. Learn. Res. 2003, 3:993-1022.
9. Hofmann T. Probabilistic latent semantic indexing. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 1999, 50-57. ACM.
10. Le Q., Mikolov T. Distributed representations of sentences and documents. Proceedings of the 31st International Conference on Machine Learning (ICML-14) 2014, 1188-1196.
11. Mikolov T., Sutskever I., Chen K., Corrado G.S., Dean J. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 2013, 3111-3119.
12. Collobert R., Weston J., Bottou L., Karlen M., Kavukcuoglu K., Kuksa P. Natural language processing (almost) from scratch. J. Mach. Learn. Res. 2011, 12:2493-2537.
13. Turian J., Ratinov L., Bengio Y. Word representations: a simple and general method for semi-supervised learning. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics 2010, 384-394. Association for Computational Linguistics.
14. Turney P.D., Pantel P., et al. From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 2010, 37(1):141-188.
15. Dai A.M., Olah C., Le Q.V., Corrado G.S. Document embedding with paragraph vectors. Proceedings of the NIPS Deep Learning Workshop 2014.
16. Wallach H.M. Topic modeling: beyond bag-of-words. Proceedings of the 23rd International Conference on Machine Learning 2006, 977-984. ACM.
17. Wang X., McCallum A., Wei X. Topical n-grams: phrase and topic discovery, with an application to information retrieval. Proceedings of the Seventh IEEE International Conference on Data Mining 2007, 697-702. IEEE.
18. Dhillon I.S., Fan J., Guan Y. Efficient clustering of very large document collections. Data Mining for Scientific and Engineering Applications 2001, 357-381. Springer.
19. Fu J., Lee S. Certainty-enhanced active learning for improving imbalanced data classification. Proceedings of the 11th International Conference on Data Mining Workshops 2011, 405-412. IEEE.
20. A.K. McCallum, Mallet: A Machine Learning for Language Toolkit. <>. http://mallet.cs.umass.edu.
21. Fan R.-E., Chang K.-W., Hsieh C.-J., Wang X.-R., Lin C.-J. Liblinear: a library for large linear classification. J. Mach. Learn. Res. 2008, 9:1871-1874.
22. Wallace B.C., Small K., Brodley C.E., Lau J., Trikalinos T.A. Modeling annotation time to reduce workload in comparative effectiveness reviews. Proceedings of the 1st ACM International Health Informatics Symposium 2010, 28-35. ACM.
23. Yu W., Clyne M., Dolan S.M., Yesupriya A., Wulf A., Liu T., Khoury M.J., Gwinn M. GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinform. 2008, 9(1):205.
24. US National Library of Medicine National Institutes of Health. <> (accessed 2016-01-05). http://www.ncbi.nlm.nih.gov/.
25. Embase: Biomedical Answers. <> (accessed 2016-01-05). http://www.embase.com/.
26. Fineout-Overholt E., Melnyk B.M., Schultz A. Transforming health care from the inside out: advancing evidence-based practice in the 21st century. J. Prof. Nurs. 2005, 21(6):335-344.