Empirical distributional semantics: Methods and biomedical applications

Cohen, Trevor; Widdows, Dominic

doi:10.1016/j.jbi.2009.02.002

Cited by 157 publications

(90 citation statements)

References 69 publications

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“…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. This approach is foundational to a number of higher-level tasks including information retrieval, word sense ambiguity resolution, automatic synonym generation and recognition, and literature-based knowledge discovery, among many others (see Cohen and Widdows, 2009 for a comprehensive review).…”

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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“…The latent variables which are considered in PLSA correspond to topics. The probabilistic model relies on two conditional probabilities: the probability that a word is associated to a given topic and the probability that a document refers to a topic -refer to the work of Cohen and Widdows (2009) for details.…”

Section: The Probabilistic Approachmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

166

102

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Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

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“…The most representative models include Latent Sematic Analysis (LSA) [2] based on statistical analysis; Random Indexing [16], which employs random sparse vectors and random permutations; and BEAGLE [20], which computes vectors using circular convolution. For recent surveys of semantic space models, see [17,18].…”

Section: Vector Representations and Reduced Descriptionsmentioning

confidence: 99%

Modular Composite Representation

Snaider

Franklin

2014

Cogn Comput

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Abstract. High-dimensional vector spaces have noteworthy properties that make them attractive for representation models. A reduced description model is a mechanism for encoding complex structures as single high-dimensional vectors. Moreover, these vectors can be used to directly process complex operations such as analogies, inferences, and structural comparisons. Also, it is possible to reconstruct the whole structure from the reduced description vector. Here we introduce the Modular Composite Representation (MCR) a new reduced description model that employs long integer vectors. We also describe several experiments with them and give a theoretical analysis of the distance distribution in this vector space and of properties of this representation. Finally, we compare MCR with other two reduced description models: Spatter Code and Holographic Reduced Representation.

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Empirical distributional semantics: Methods and biomedical applications

Cited by 157 publications

References 69 publications

Corpus domain effects on distributional semantic modeling of medical terms

Corpus domain effects on distributional semantic modeling of medical terms

Semantic Similarity from Natural Language and Ontology Analysis

Modular Composite Representation

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