1,721,098 research outputs found
An automatic method for the lexical disambiguation of names
Full text linkEste artículo presenta un método completamente automático que resuelve la desambiguación léxica de nombres calculando la densidad conceptual de cada uno de los sentidos del nombre a desambiguar. La evaluación del método se ha realizado sobre el corpus SemCor con un contexto de sólo dos nombres, obteniendo una precisión de 81.5% y un recall de 60.25%.Palabras clave: desambiguación léxica de nombres, densidad conceptual.This article presents a completely automatic method that solves the lexical disambiguation of names by calculating the conceptual density of each of the senses of the name to be disambiguated. The evaluation of the method has been carried out on the SemCor corpus with a context of only two names, obtaining an accuracy of 81.5% and a recall of 60.25%. Keywords: lexical disambiguation of names, conceptual density
Empowering detection of malicious social bots and content spammers on Twitter by sentiment analysis
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DeepOntoNLP & X-SENTIMENT 2021: Advances in Semantics and Explainability for NLP: Joint proceedings of the DeepOntoNLP and X-SENTIMENT Workshops
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Mining scholarly data for fine-grained knowledge graph construction
Full text linkKnowledge graphs (KG) are large networks of entities and relationships, typically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain an explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications, and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyze an automatically generated Knowledge Graph including 10 425 entities and 25 655 relationships derived from 12 007 publications in the field of Semantic Web, and iv) discuss some open problems that have not been solved yet
Desambiguación de topónimos en la recuperación de información
No full textTesis doctoral (con mención de doctorado europeo) en Informática realizada por Davide Buscaldi y dirigida por el doctor Paolo Rosso (Univ. Politécnica de Valencia). El acto de defensa de la tesis tuvo lugar en Valencia en Octubre de 2010 ante el tribunal formado por los doctores: Paul David Clough (University of Sheffield), Ross Purves (Universität Zürich), Emilio Sanchis Arnal (Univ. Politécnica de Valencia), Mark Sanderson (Royal Melbourne Institute of Technology), Diana Santos (Sintef-ICT, Oslo). La mención europea se obtuvo a través de una estancia en el FBK-IRST (Italia) bajo la dirección de Bernardo Magnini. La calificación obtenida fue de Sobresaliente Cum Laude.Ph.D. thesis (European doctorate mention) in Computer Science written by Davide Buscaldi under the supervision of Dr. Paolo Rosso (Univ. Politécnica de Valencia). The author was examined in Valencia in October 2010 by a panel composed by the following doctors: Paul David Clough (University of Sheffield), Ross Purves (Universität Zürich), Emilio Sanchis Arnal (Univ. Politécnica de Valencia), Mark Sanderson (Royal Melbourne Institute of Technology), Diana Santos (Sintef-ICT, Oslo). The European mentions was received after a 3 months stage at the FBK-IRST (Italy) under the guidance of Bernardo Magnini. The obtained grade was Cum Laude.Thesis supported by a FPI Grant of the Valencian government (ref. BFPI06/97)
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Mining Scholarly Publications for Scientific Knowledge Graph Construction
Full text linkIn this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, and iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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