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Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension.

ABSTRACT: In two experiments, we attempted to replicate and extend findings by Günther et al. (2016) that word similarity measures obtained from distributional semantics models-Latent Semantic Analysis (LSA) and Hyperspace Analog to Language (HAL)-predict lexical priming effects. To this end, we used the pseudo-random method to generate item material while systematically controlling for word similarities introduced by Günther et al. (2016) which was based on LSA cosine similarities (Experiment 1) and HAL cosine similarities (Experiment 2). Extending the original study, we used semantic spaces created from far larger corpora, and implemented several additional methodological improvements. In Experiment 1, we only found a significant effect of HAL cosines on lexical decision times, while we found significant effects for both LSA and HAL cosines in Experiment 2. As further supported by an analysis of the pooled data from both experiments, this indicates that HAL cosines are a better predictor of priming effects than LSA cosines. Taken together, the results replicate the finding that priming effects can be predicted from distributional semantic similarity measures.

SUBMITTER: Gunther F

PROVIDER: S-EPMC5076462 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension.

Günther Fritz F Dudschig Carolin C Kaup Barbara B

Frontiers in psychology 20161024

In two experiments, we attempted to replicate and extend findings by Günther et al. (2016) that word similarity measures obtained from distributional semantics models-Latent Semantic Analysis (LSA) and Hyperspace Analog to Language (HAL)-predict lexical priming effects. To this end, we used the pseudo-random method to generate item material while systematically controlling for word similarities introduced by Günther et al. (2016) which was based on LSA cosine similarities (Experiment 1) and HAL ...[more]

PMID: 27822195

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Project description:MotivationAutomatically 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.ResultsWe 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 = 0.62 for similarity and 0.58 for relatedness) are on par with measures computed from clinical reports (rho = 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.Availability and implementationThe software and reference standards used in this study to evaluate semantic similarity and relatedness measures are publicly available as detailed in the article.Contactpakh0002@umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Dataset Information

Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension.

Publications

Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension.

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