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12 Publikationen

2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2916985 OA
Jebbara, S., Basile, V., Cabrio, E., & Cimiano, P. (2019). Extracting common sense knowledge via triple ranking using supervised and unsupervised distributional models. Semantic Web, 10(1), 139-158. doi:10.3233/SW-180302
PUB | PDF | DOI | WoS
 
2019 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2934325 OA
Jebbara, S., & Cimiano, P. (2019). Zero-Shot Cross-Lingual Opinion Target Extraction. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933089 OA
Hakimov, S., Jebbara, S., & Cimiano, P. (2019). Evaluating Architectural Choices for Deep Learning Approaches for Question Answering over Knowledge Bases. Presented at the The 13th IEEE International Conference on ​Semantic Computing, Newport Beach, California.
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913141 OA
Hakimov, S., Jebbara, S., & Cimiano, P. (2017). AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data. In C. d'Amato, M. Fernandez, & V. Tamma (Eds.), Lecture Notes in Computer Science: Vol. 10587. The Semantic Web - ISWC 2017. 16th International Semantic Web Conference. Proceedings (pp. 329-346). Cham: Springer. doi:10.1007/978-3-319-68288-4_20
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2017 | Konferenzbeitrag | PUB-ID: 2913711 OA
Jebbara, S., & Cimiano, P. (2017). Improving Opinion-Target Extraction with Character-Level Word Embeddings. Proceedings of the First Workshop on Subword and Character Level Models in NLP Association for Computational Linguistics.
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907859 OA
Hartung, M., Kaupmann, F., Jebbara, S., & Cimiano, P. (2017). Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases. Proceedings of the 15th Meeting of the European Chapter of the Association for Computational Linguistics (EACL), Vol. 1 (Long Papers), 54-64.
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2016 | Datenpublikation | PUB-ID: 2902978 OA
Hakimov, S., ter Horst, H., Jebbara, S., Hartung, M., & Cimiano, P. (2016). Research Data - Combining Textual and Graph-based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models. Bielefeld University. doi:10.4119/unibi/2902978
PUB | Dateien verfügbar | DOI
 
2016 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2906020 OA
Jebbara, S., & Cimiano, P. (2016). Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture. Presented at the European Semantic Web Conference (ESWC)
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904445 OA
Jebbara, S., & Cimiano, P. (2016). Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture. In H. Sack, S. Dietze, A. Tordai, & C. Lange (Eds.), Communications in Computer and Information Science: Vol. 641. Semantic Web Challenges. Third SemWebEval Challenge at ESWC 2016. Revised Selected Papers (pp. 153-170). Cham: Springer. doi:10.1007/978-3-319-46565-4_12
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905552 OA
Hakimov, S., ter Horst, H., Jebbara, S., Hartung, M., & Cimiano, P. (2016). Combining Textual and Graph-based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models. Knowledge Engineering and Knowledge Management, Lecture Notes in Computer Science, 10024, 288-302. Springer. doi:10.1007/978-3-319-49004-5_19
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2016 | Konferenzbeitrag | PUB-ID: 2905625 OA
Jebbara, S., & Cimiano, P. (2016). Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture. ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016), 1123--1131. doi:10.3233/978-1-61499-672-9-1123
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2016 | Konferenzbeitrag | PUB-ID: 2905857
Basile, V., Jebbara, S., Cabrio, E., & Cimiano, P. (2016). Populating a Knowledge Base with Object-Location Relations using Distributional Semantics. Proceedings of 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW). doi:10.1007/978-3-319-49004-5_3
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