All publications sorted by author
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| , , and , How to make the most of NE dictionaries in statistical NER (2008), in: BMC Bioinformatics, 9:Suppl 11(S5) |
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| , , and , How to Make the Most of NE Dictionaries in Statistical NER, in: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (BioNLP 2008), pages 63--70, Association for Computational Linguistics, 2008 |
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| , and , Extracting Secondary Bio-Event Arguments with Extraction Constraints (2011), in: Computational Intelligence, 27:4(702-721) |
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| , , , , , and , Distributed Document and Phrase Co-embeddings for Descriptive Clustering, in: Proceedings of EACL, pages 991-1001, 2017 |
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| , , , , and , AkaneRE Relation Extraction: Protein Interaction and Normalization in the BioCreAtIvE II.5 Challenge, in: BioCreative II.5 Workshop 2009 special session | Digital Annotations, pages 33, CNIO, 2009 |
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| , , , , , , , , and , Connecting Text Mining and Pathways using the PathText Resource, in: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008), pages 1736-1740, European Language Resources Association (ELRA), 2008 |
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| , , , , , , , , and , PathText: Text Mining Tools Integrated with Biological Pathway, in: Genomes to Systems Conference 2008 Handbook, pages 65, The Consortium for Post-Genome Science, 2008 |
| , , and , From Protein-Protein Interaction to Molecular Event Extraction, in: Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task, pages 103--106, 2009 |
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| , and , Syntactic features for protein-protein interaction extraction, in: Short Paper Proceedings of the 2nd International Symposium on Languages in Biology and Medicine (LBM 2007), pages 6.1-6.14, CEUR Workshop Proceedings (CEUR-WS.org), 2008 |
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| , , , , and , Extracting Protein-Interactions from Text with the Unified AkaneRE Event Extraction System (2010), in: Transactions on Computational Biology and Bioinformatics (TCBB), BioCreative II.5 Special Issue, 7:3(442-453) |
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| and , RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification, in: Proceedings of LREC 2022, pages 6317-6327, 2022 |
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| , , and , A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome (2022), in: Int. J. Environ. Res. Public Health, 19:14(8544) |
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| , , , and , Classifying suicide-related content and emotions on Twitter using Graph Convolutional Neural Networks (2022), in: IEEE Transactions on Affective Computing(1-12) |
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| , , , , and , Identification of Research Hypotheses and New Knowledge from Scientific Literature (2018), in: BMC Medical Informatics and Decision Making, 18(46) |
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| , , , , , and , A Text Mining Pipeline Using Active and Deep Learning for Curating Information in Computational Neuroscience (2018), in: Neuroinformatics |
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| , , , , , , and , A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database, in: Proceedings of LREC 2018, pages 280-285, 2018 |
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| , , , , and , Facilitating and promoting web annotation with Argo, in: Proceedings of I Annotate 2016, 2016 |
| , , , and , An ontology-based disambiguation of terms, in: Proceedings of the Workshop on Mining the Pharmacogenomics Literature, Pacific Symopsium on Biocomputing, 2011 |
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| , and , Thalia: Semantic search engine for biomedical abstracts (2018), in: Bioinformatics, 35:10(1799–1801) |
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| , , and , LitPathExplorer: A Confidence-based Visual Text Analytics Tool for Exploring Literature-Enriched Pathway Models (2017), in: Bioinformatics, 34:8(1389-1397) |
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| and , Enhancing Stress Detection on Social Media Through Multi-Modal Fusion of Text and Synthesized Visuals, in: Proceedings of BioNLP 2025, In Press |
| , , , , , , , , , and , Are all flavonoids safe for use in Cosmetics?, in: Proceedings of the 30th IFSCC Congress, pages Poster S2-296, 2018 |
| , , , , , , , , , , , , and , Role of in silico tools and text mining in the risk assessment of selected alkaloids (2018), in: Toxicology Letters, 295:Supplement 1(S159) |
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| and , A flexible measure of contextual similarity for biomedical terms., in: Proceedings of the Pacific Symposium on Biocomputing 10, pages 197--208, 2005 |
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| and , Using automatically learnt verb selectional preferences for classification of biomedical terms (2004), in: Journal of Biomedical Informatics, 37:6(483--497) |
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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 | 526-550 | 551-566 |