All publications
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2016
Identifying content types of messages related to Open Source Software projects, in: Proceedings of LREC 2016, pages 1837-1844, 2016 | , and ,
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Learning to recognise named entities in Tweets by exploiting weakly labelled data, in: Proceedings of the 2nd Workshop on Noisy User-generated Text (W-NUT 2016), 2016 | , and ,
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Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource (2016), in: PLOS ONE, 11:9(e0162287) | , and ,
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NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features, in: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), pages 614-620, 2016 | , , , and ,
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Text Mining for Semantic Search in Europe PubMed Central Labs, in: Working with Text: Tools, Techniques and Approaches for Text Mining, pages 111-132, Elsevier, 2016 | , , , and ,
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Text Mining Resources for the Life Sciences (2016), in: Database: The Journal of Biological Databases and Curation(baw145) | , , , , , , and ,
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Text Mining the History of Medicine (2016), in: PLOS One, 11:1(e0144717) | , , , , , , , and ,
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Text Mining Workflows for Indexing Archives with Automatically Extracted Semantic Metadata, in: Proceedings of the 20th International Conference on Theory and Practice of Digital Libraries (TPDL 2016), pages 471–473, Springer, 2016 | , , and ,
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The strategic impact of META-NET on the regional, national and international level (2016), in: Language Resources and Evaluation, 50:2(351-374) | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and ,
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Topic Detection Using Paragraph Vectors to Support Active Learning in Systematic Reviews (2016), in: Journal of Biomedical Informatics, 62(59–65) | , , and ,
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Using text mining to facilitate study identification in public health systematic reviews (2016), in: Guidelines International Network (G-I-N) conference | , , , , , , and ,
2015
A Cross-lingual Similarity Measure for Detecting Biomedical Term Translations (2015), in: PLOS ONE, 10:6(e0126196) | , and ,
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A Survey of Quality Prediction of Product Reviews (2015), in: International Journal of Advanced Computer Science and Applications, 6:11(49-58) | , and ,
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Adaptable, high recall, event extraction system with minimal configuration (2015), in: BMC Bioinformatics, 16:Suppl 10.(S7) | and ,
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Adapting ChER for the recognition of chemical mentions in patents, in: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, Seville, Spain, pages 149-153, 2015 | and ,
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Augmenting the Medical Subject Headings vocabulary with semantically rich variants to improve disease mention normalisation, in: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, Seville, Spain, pages 311-316, 2015 | and ,
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Bilingual term alignment from comparable corpora in English discharge summary and Chinese discharge summary (2015), in: BMC Bioinformatics, 16(149) | , , , , , , and ,
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Boosting Drug Named Entity Recognition using an Aggregate Classifier (2015), in: Artificial Intelligence in Medicine (AIIM), Special issue of ICHI 2013 conference | , , and ,
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Customised OCR Correction for Historical Medical Text, in: Proceedings of DigitalHeritage 2015, pages 35 - 42, 2015 | , and ,
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Development of bespoke machine learning and biocuration workflows in a BioC-supporting text mining workbench, in: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, Seville, Spain, pages 51-56, 2015 | , and ,
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Europe PMC: a full-text literature database for the life sciences and platform for innovation (2015), in: Nucleic Acids Research, 43:D1(D1042-D1048) | ,
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Event Extraction in pieces: Tackling the partial event identification problem on unseen corpora, in: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015), pages 31-41, 2015 | and ,
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Mining the biomedical literature, in: Healthcare Data Analytics, pages 251-308, CRC Press, 2015 | , , , , , , and ,
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Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics (2015), in: Journal of Cheminformatics, 7:Suppl 1(S6) | , and ,
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| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-425 | 426-450 | 451-475 | 476-500 | 501-525 | 526-546 |