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SemWebEval@ESWC 2014: Anissaras, Crete, Greece
- Valentina Presutti, Milan Stankovic, Erik Cambria, Iván Cantador, Angelo Di Iorio, Tommaso Di Noia, Christoph Lange, Diego Reforgiato Recupero, Anna Tordai:
Semantic Web Evaluation Challenge - SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers. Communications in Computer and Information Science 475, Springer 2014, ISBN 978-3-319-12023-2
Concept Level Sentiment Analysis
- Diego Reforgiato Recupero, Erik Cambria:
ESWC'14 Challenge on Concept-Level Sentiment Analysis. 3-20 - Mauro Dragoni, Andrea G. B. Tettamanzi, Célia da Costa Pereira:
A Fuzzy System for Concept-Level Sentiment Analysis. 21-27 - Nir Ofek, Lior Rokach:
Unsupervised Fine-Grained Sentiment Analysis System Using Lexicons and Concepts. 28-33 - Anni Coden, Daniel Gruhl, Neal Lewis, Pablo N. Mendes, Meena Nagarajan, Cartic Ramakrishnan, Steve Welch:
Semantic Lexicon Expansion for Concept-Based Aspect-Aware Sentiment Analysis. 34-40 - Soujanya Poria, Nir Ofek, Alexander F. Gelbukh, Amir Hussain, Lior Rokach:
Dependency Tree-Based Rules for Concept-Level Aspect-Based Sentiment Analysis. 41-47 - Shafqat Mumtaz Virk, Yann-Huei Lee, Lun-Wei Ku:
Sinica Semantic Parser for ESWC'14 Concept-Level Semantic Analysis Challenge. 48-52 - Jay Kuan-Chieh Chung, Chi-En Wu, Richard Tzong-Han Tsai:
Polarity Detection of Online Reviews Using Sentiment Concepts: NCU IISR Team at ESWC-14 Challenge on Concept-Level Sentiment Analysis. 53-58
Semantic Publishing
- Christoph Lange, Angelo Di Iorio:
Semantic Publishing Challenge - Assessing the Quality of Scientific Output. 61-76 - Raúl Palma, Piotr Holubowicz, Óscar Corcho, José Manuél Gómez-Pérez, Cezary Mazurek:
ROHub - A Digital Library of Research Objects Supporting Scientists Towards Reproducible Science. 77-82 - Francesco Ronzano, Gerard Casamayor del Bosque, Horacio Saggion:
Semantify CEUR-WS Proceedings: Towards the Automatic Generation of Highly Descriptive Scholarly Publishing Linked Datasets. 83-88 - Maxim Kolchin, Fedor Kozlov:
A Template-Based Information Extraction from Web Sites with Unstable Markup. 89-94 - Rinke Hoekstra, Paul Groth, Marat Charlaganov:
Linkitup: Semantic Publishing of Research Data. 95-100 - Francesco Osborne, Enrico Motta:
Understanding Research Dynamics. 101-107 - Iana Atanassova, Marc Bertin:
Semantic Facets for Scientific Information Retrieval. 108-113 - Anastasia Dimou, Miel Vander Sande, Pieter Colpaert, Laurens De Vocht, Ruben Verborgh, Erik Mannens, Rik Van de Walle:
Extraction and Semantic Annotation of Workshop Proceedings in HTML Using RML. 114-119 - Marc Bertin, Iana Atanassova:
Extraction and Characterization of Citations in Scientific Papers. 120-126
Linked-Data Enabled Recommender Systems
- Tommaso Di Noia, Iván Cantador, Vito Claudio Ostuni:
Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation. 129-143 - Ladislav Peska, Peter Vojtás:
Hybrid Recommending Exploiting Multiple DBPedia Language Editions. 144-149 - Petar Ristoski, Eneldo Loza Mencía, Heiko Paulheim:
A Hybrid Multi-strategy Recommender System Using Linked Open Data. 150-156 - Nicholas Ampazis, Theodoros Emmanouilidis:
Exploring Semantic Features for Producing Top-N Recommendation Lists from Binary User Feedback. 157-162 - Pierpaolo Basile, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Fedelucio Narducci, Giovanni Semeraro:
Content-Based Recommender Systems + DBpedia Knowledge = Semantics-Aware Recommender Systems. 163-169 - Benjamin Heitmann, Conor Hayes:
SemStim at the LOD-RecSys 2014 Challenge. 170-175 - Michael Schuhmacher, Christian Meilicke:
Popular Books and Linked Data: Some Results for the ESWC'14 RecSys Challenge. 176-181 - Valentina Maccatrozzo, Davide Ceolin, Lora Aroyo, Paul Groth:
A Semantic Pattern-Based Recommender. 182-187 - Andrés Moreno, Christian Ariza-Porras, Paula Andrea Lago, Claudia Lucía Jiménez-Guarín, Harold Castro, Michel Riveill:
Hybrid Model Rating Prediction with Linked Open Data for Recommender Systems. 193-198 - Omar U. Florez:
Deep Learning of Semantic Word Representations to Implement a Content-Based Recommender for the RecSys Challenge'14. 199-204
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