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Evaluating semantic textual similarity in clinical sentences using deep learning and sentence embeddings
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Statistical Complexity Analysis of Turing Machine tapes with Fixed Algorithmic Complexity Using the Best-Order Markov Model
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In: Entropy (Basel) (2020)
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Understanding Depression from Psycholinguistic Patterns in Social Media Texts
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Biomedical word sense disambiguation with word embeddings
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Abstract:
There is a growing need for automatic extraction of information and knowledge from the increasing amount of biomedical and clinical data produced, namely in textual form. Natural language processing comes in this direction, helping in tasks such as information extraction and information retrieval. Word sense disambiguation is an important part of this process, being responsible for assigning the proper concept to an ambiguous term. In this paper, we present results from machine learning and knowledge-based algorithms applied to biomedical word sense disambiguation. For the supervised machine learning algorithms we used word embeddings, calculated from the full MEDLINE literature database, as global features and compare the results to the use of local unigram and bigram features. For the knowledge-based method we represented the textual definitions of biomedical concepts from the UMLS database as word embedding vectors, and combined this with concept associations derived from the MeSH term co-occurrences. Both the machine learning and the knowledge-based results indicate that word embeddings are informative and improve the biomedical word disambiguation accuracy. Applied to the reference MSH WSD data set, our knowledge-based approach achieves 85.1% disambiguation accuracy, which is higher than some previously proposed approaches that do not use machine-learning strategies. ; published
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Keyword:
Biomedical word sense disambiguation; Word embeddings
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URL: http://hdl.handle.net/10773/25112 https://doi.org/10.1007/978-3-319-60816-7_33
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Evaluation of word embedding vector averaging functions for biomedical word sense disambiguation
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Overview of the interactive task in BioCreative V
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In: ISSN: 1758-0463 ; EISSN: 1758-0463 ; Database - The journal of Biological Databases and Curation ; https://hal.archives-ouvertes.fr/hal-01469079 ; Database - The journal of Biological Databases and Curation, Oxford University Press, 2016, 2016, ⟨10.1093/database/baw119⟩ ; https://academic.oup.com/database/article-lookup/doi/10.1093/database/baw119 (2016)
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The CHEMDNER corpus of chemicals and drugs and its annotation principles
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An Overview of Biomolecular Event Extraction from Scientific Documents
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Mining biomedical information from scientific literature ; Mineração de informação biomédica a partir de literatura científica
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Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases
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In: Krallinger, Martin; Vazquez, Miguel; Leitner, Florian; Salgado, David; Chatr-aryamontri, Andrew; Winter, Andrew; et al.(2011). Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases. BMC Bioinformatics, 12(Suppl 8), S3. doi: http://dx.doi.org/10.1186/1471-2105-12-S8-S3. Retrieved from: http://www.escholarship.org/uc/item/44z3n3v1 (2011)
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The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-01780325 ; BMC Bioinformatics, BioMed Central, 2011, 12 (Suppl 8), ⟨10.1186/1471-2105-12-S8-S3⟩ (2011)
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Corpógrafo and NooJ: using linguistic resources to obtain aligned concordances from corpora
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Interpretações temporais de sintagmas com a preposição Em
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In: http://aleph.letras.up.pt/F?func=find-b&find_code=SYS&request=000145509 (1998)
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Linguística e informática : perspectivas recentes do computador em linguística aplicada e descritiva
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In: http://aleph.letras.up.pt/F?func=find-b&find_code=SYS&request=000189478 (1989)
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