Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

Resnik, Philip

doi:10.1613/jair.514

Cited by 1,681 publications

(1,474 citation statements)

References 54 publications

(50 reference statements)

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“…Automated approaches for representing the semantic content of terms and similarity and relatedness between them have been widely used in a number of Natural Language Processing (NLP) applications in both general English (Budanitsky and Hirst, 2006;Landauer, 2006;Resnik, 1999;Weeds and Weir, 2005) and specialized terminological domains such as bioinformatics (Ferreira et al, 2013;Lord et al, 2003;Mazandu et al, 2016;Wang et al, 2007;Yang et al, 2012) and medicine (Garla and Brandt, 2012;Lee et al, 2008;Liu et al, 2012;Pakhomov et al, 2010;Pedersen et al, 2007;Sajadi, 2014). A subset of these methods, distributional semantics, relies on the co-occurrence information between words obtained from large corpora of text and makes the assumption that words with similar or related meanings tend to occur in similar contexts.…”

Section: Introductionmentioning

confidence: 99%

Corpus domain effects on distributional semantic modeling of medical terms

et al. 2016

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Motivation: Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available; however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated. Results: We constructed neural network representations of clinical terms found in a publicly available benchmark dataset manually labeled for semantic similarity and relatedness. Similarity and relatedness measures computed from text corpora in three domains (Clinical Notes, PubMed Central articles and Wikipedia) were compared using the benchmark as reference. We found that measures computed from full text of biomedical articles in PubMed Central repository (rho ¼ 0.62 for similarity and 0.58 for relatedness) are on par with measures computed from clinical reports (rho ¼ 0.60 for similarity and 0.57 for relatedness). We also evaluated the use of neural network based relatedness measures for query expansion in a clinical document retrieval task and a biomedical term word sense disambiguation task. We found that, with some limitations, biomedical articles may be used in lieu of clinical reports to represent the semantics of clinical terms and that distributional semantic methods are useful for clinical and biomedical natural language processing applications.

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Section: Introductionmentioning

confidence: 99%

Corpus domain effects on distributional semantic modeling of medical terms

et al. 2016

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“…For every branch (biological process, molecular function and cellular component ) of GO taxonomy, a net is constructed by considering the GO terms the 8449 genes are annotated with, and by setting an edge between two genes if they share at least one annotation in the corresponding GO branch. The edge weight is the maximum Rensik semantic similarity [54] between all the terms for which the two genes are both annotated.…”

Section: Benchmark Datamentioning

confidence: 99%

“…Resnik : Resnik [54]. The similarity of two nodes is the information content of their minimum common ancestor:…”

Section: Disease Semantic Similaritiesmentioning

confidence: 99%

Gene2DisCo: Gene to disease using disease commonalities

Frasca

2017

Artificial Intelligence in Medicine

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“…Among the earlier developed methods, an IC based measure called the Resnik measure has showed strong correlations between its results and gene expression similarities on yeast [16,22]. Mathematically, given a GO term t , its IC is defined as a negative log likelihood IC ( t ) = − log( |G t |/|G root | ), where G t and G root are the sets of genes annotated to term t and the root term (including all of its descendants) respectively.…”

Section: Introductionmentioning

confidence: 99%

Towards integrative gene functional similarity measurement

2014

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BackgroundIn Gene Ontology, the "Molecular Function" (MF) categorization is a widely used knowledge framework for gene function comparison and prediction. Its structure and annotation provide a convenient way to compare gene functional similarities at the molecular level. The existing gene similarity measures, however, solely rely on one or few aspects of MF without utilizing all the rich information available including structure, annotation, common terms, lowest common parents.ResultsWe introduce a rank-based gene semantic similarity measure called InteGO by synergistically integrating the state-of-the-art gene-to-gene similarity measures. By integrating three GO based seed measures, InteGO significantly improves the performance by about two-fold in all the three species studied (yeast, Arabidopsis and human).ConclusionsInteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.

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Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

Cited by 1,681 publications

References 54 publications

Corpus domain effects on distributional semantic modeling of medical terms

Corpus domain effects on distributional semantic modeling of medical terms

Gene2DisCo: Gene to disease using disease commonalities

Towards integrative gene functional similarity measurement

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