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Adversarial Constraint Evaluation on Biomedical Text Mining
2021Language model pre-training has been shown to be effective for improving many NLP tasks, relying on its ability of representing complex context. The BERT language model (LM) has achieved remarkable results in standard performance indicators for tasks such as Named Entity Recognition (NER) and Semantic Text Similarity (STS), and has made significant ...
Yashen Wang, Huanhuan Zhang
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Terminological resources for text mining over biomedical scientific literature
Artificial Intelligence in Medicine, 2011We present a combined terminological resource for text mining over biomedical literature. The purpose of the resource is to allow the detection of mentions of specific biological entities in scientific publications, and their grounding to widely accepted identifiers.
Rinaldi, Fabio, Kaljurand, K, Saetre, R
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Biomedical Hypothesis Generation by Text Mining and Gene Prioritization
Protein & Peptide Letters, 2013Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain.
Petrič I +3 more
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Biomedical Text Mining: Experience and Practical Approach
2015 3rd International Conference on Applied Computing and Information Technology/2nd International Conference on Computational Science and Intelligence, 2015The fields of biomedical researches included biology and medicine has resulted in a sheer amount of published reports, and papers. Above all biomedical text mining has emerged as a vital research domain that has an impact in the project development of these research areas.
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Introduction to Biomedical Literature Text Mining: Context and Objectives
2014If you are reading this, you know how important it is and almost certainly look to the biomedical literature for a large part of the information you need. We work hard to find more and more biomedical literature, seeking new content from multiple sources. But, can there be too much of a good thing? Most science is reductionist by nature.
Jeffrey D, Saffer, Vicki L, Burnett
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Text Mining for Automatic Lexical Analysis of Layman Text of Biomedical Argument
2009Despite various efforts to improve reliability of health care material on the world wide web in recent years, progress on this issue has been limited. Thus far, a variety of terms intended to describe this issue, including quality, trustworthiness, and credibility have been used.
D. Defilippi +2 more
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Mining molecular binding terminology from biomedical text.
Proceedings. AMIA Symposium, 2000Automatic access to information regarding macromolecular binding relationships would provide a valuable resource to the biomedical community. We report on a pilot project to mine such information from the molecular biology literature. The program being developed takes advantage of natural language processing techniques and is supported by two ...
T C, Rindflesch, L, Hunter, A R, Aronson
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2018
Most word embedding methods are proposed with general purpose which take a word as a basic unit and learn embeddings by words’ external contexts. However, in the field of biomedical text mining, there are many biomedical entities and syntactic chunks which can enrich the semantic meaning of word embeddings. Furthermore, large scale background texts for
Lishuang Li, Jia Wan, Degen Huang
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Most word embedding methods are proposed with general purpose which take a word as a basic unit and learn embeddings by words’ external contexts. However, in the field of biomedical text mining, there are many biomedical entities and syntactic chunks which can enrich the semantic meaning of word embeddings. Furthermore, large scale background texts for
Lishuang Li, Jia Wan, Degen Huang
openaire +1 more source