780 research outputs found
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain
Eliciting semantic similarity between concepts in the biomedical domain
remains a challenging task. Recent approaches founded on embedding vectors have
gained in popularity as they risen to efficiently capture semantic
relationships The underlying idea is that two words that have close meaning
gather similar contexts. In this study, we propose a new neural network model
named MeSH-gram which relies on a straighforward approach that extends the
skip-gram neural network model by considering MeSH (Medical Subject Headings)
descriptors instead words. Trained on publicly available corpus PubMed MEDLINE,
MeSH-gram is evaluated on reference standards manually annotated for semantic
similarity. MeSH-gram is first compared to skip-gram with vectors of size 300
and at several windows contexts. A deeper comparison is performed with tewenty
existing models. All the obtained results of Spearman's rank correlations
between human scores and computed similarities show that MeSH-gram outperforms
the skip-gram model, and is comparable to the best methods but that need more
computation and external resources.Comment: 6 pages, 2 table
Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy
Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd
BIOMEDICAL WORD SENSE DISAMBIGUATION WITH NEURAL WORD AND CONCEPT EMBEDDINGS
Addressing ambiguity issues is an important step in natural language processing (NLP) pipelines designed for information extraction and knowledge discovery. This problem is also common in biomedicine where NLP applications have become indispensable to exploit latent information from biomedical literature and clinical narratives from electronic medical records. In this thesis, we propose an ensemble model that employs recent advances in neural word embeddings along with knowledge based approaches to build a biomedical word sense disambiguation (WSD) system. Specifically, our system identities the correct sense from a given set of candidates for each ambiguous word when presented in its context (surrounding words). We use the MSH WSD dataset, a well known public dataset consisting of 203 ambiguous terms each with nearly 200 different instances and an average of two candidate senses represented by concepts in the unified medical language system (UMLS). We employ a popular biomedical concept, Our linear time (in terms of number of senses and context length) unsupervised and knowledge based approach improves over the state-of-the-art methods by over 3% in accuracy. A more expensive approach based on the k-nearest neighbor framework improves over prior best results by 5% in accuracy. Our results demonstrate that recent advances in neural dense word vector representations offer excellent potential for solving biomedical WSD
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