Navegación

Búsqueda

Búsqueda avanzada

tESA: using semantics of scientific articles to approximate semantic relatedness

Resumen:

Short abstract Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts is an important part of many text and knowledge processing tasks of crucial importance in the ever growing domain of biomedical informatics. In this paper we present tESA, an extension to a well known Explicit Semantic Relatedness (ESA) method, which leverages the semantics of a corpus of scientific documents to improve the quality of the relatedness approximation for biomedical domain. In our extension we use two separate sets of vectors, corresponding to different sections of the articles from the underlying corpus of documents, as opposed to the original method, which only uses a single vector space. Our findings suggest that extending the original ESA methodology with the use of title vectors of the documents of scientific corpora may be used to enhance the performance of a distributional semantic relatedness measures. Background Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in the ever growing domain of biomedical informatics. The problem of most state-of-the-art methods for calculating semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the Life Sciences has become more and more accessible, but mostly in its unstructured form – as texts in large document collections, which makes its use more challenging for automated processing. In this paper we present tESA, an extension to a well known Explicit Semantic Relatedness (ESA) method. Results In our extension we use two separate sets of vectors, corresponding to different sections of the articles from the underlying corpus of documents, as opposed to the original method, which only uses a single vector space. We present an evaluation of Life Sciences domain-focused applicability of both tESA and domain-adapted Explicit Semantic Analysis. The methods are tested against a set of standard benchmarks established for the evaluation of biomedical semantic relatedness quality. Our experiments show that the propsed method achieves results comparable with or superior to the current state-of-the-art methods. Additionally, a comparative discussion of the results obtained with tESA and ESA is presented, together with a study of the adaptability of the methods to different corpora and their performance with different input parameters. Conclusions Our findings suggest that combined use of the semantics from different sections (i.e. extending the original ESA methodology with the use of title vectors) of the documents of scientific corpora may be used to enhance the performance of a distributional semantic relatedness measures, which can be observed in the largest reference datasets. We also present the impact of the proposed extension on the size of distributional representations. Publication details The original paper tESA: a distributional measure for calculating semantic relatedness (DOI: 10.1186/s13326-016-0109-6), authored by Maciej Rybinski and José Francisco Aldana-Montes, was published online in the Journal of Biomedical Semantics on 28th of December 2016. The Journal of Biomedical Semantics currently holds (according to the latest JCR for 2015) an impact factor of 1.62, with a five-year impact factor of 2.511. The main impact factor places the Journal in the second cuartile (Q2) of its JCR-SCI category MATHEMATICAL & COMPUTATIONAL BIOLOGY. Acknowledgments Work presented here was partially supported by grants TIN2014-58304-R (Ministerio de Ciencia e Innovación), P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación) and EU FP7-KBBE-289126 (the EU 7th Framework Programme, BIOLEDGE).

Palabras Clave:

Bioinformatics - Biomedical semantics - Distributional linguistics - Explicit semantic analysis - Knowledge extraction - Semantic relatedness - Semantic similarity

Autor(es):

Handle:

11705/JISBD/2017/022

Descargas:

Este artículo tiene una licencia de uso CreativeCommons Reconocimiento (by)

Descarga el artículo haciendo click aquí.