Membri.Rossiello History
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- Text Summarizer - A centroid-based method for extractive text summarization which exploits the compositional capability of word embeddings. [https://github.com/gaetangate/text-summarizer GitHub]
- Text Summarizer - A centroid-based method for extractive text summarization which exploits the compositional capability of word embeddings. GitHub
- Text Summarizer - A centroid-based method for extractive text summarization which exploits the compositional capability of word embeddings. GitHub
- Text Summarizer - A centroid-based method for extractive text summarization which exploits the compositional capability of word embeddings. [https://github.com/gaetangate/text-summarizer GitHub]
- Text Summarizer - A centroid-based method for extractive text summarization which exploits the compositional capability of word embeddings. GitHub
- IIR 2012. 3nd Italian Information Retrieval Workshop. 26-27 Junuary 2012, Bari (IT).
- G. Rossiello, P. Basile, G. Semeraro. ''Centroid-based Text Summarization
through Compositionality of Word Embeddings.'' MultiLing 2017 Workshop in EACL 2017. Summarization and summary evaluation across source types and genres.
- G. Rossiello, P. Basile, G. Semeraro. Centroid-based Text Summarization through Compositionality of Word Embeddings. MultiLing 2017 Workshop in EACL 2017. Summarization and summary evaluation across source types and genres.
through Compositionality of Word Embeddings.'' MultiLing 2017 Workshop in EACL 2017. Summarization and summary evaluation across source types and genres.
through Compositionality of Word Embeddings.'' MultiLing 2017 Workshop in EACL 2017. Summarization and summary evaluation across source types and genres.
- BigDat 2017. International Winter School on Big Data - Bari, Italy
- G. Rossiello, P. Basile, G. Semeraro. ''Centroid-based Text Summarization
through Compositionality of Word Embeddings.'' MultiLing 2017 Workshop in EACL 2017. Summarization and summary evaluation across source types and genres.
- REDIRECT Membri.GaetanoRossiello
http://i.imgur.com/0n9fJnx.jpg
Gaetano Rossiello
PhD Student
University of Bari "Aldo Moro"
Department of Computer Science
Via E.Orabona, 4 - 70126 BARI, Italy
Phone: +39 080 5442298
e-mail: gaetano.rossiello[_AT_]uniba.it
Research Interests | Short CV | Summer Schools | Publications | Conferences | Tools
Main Research Interests
- Natural Language Processing
- Information Retrieval
- Recommender System
- Machine Learning
- Deep Learning
- NOVEMBER 2015 - Start the Ph.D. in Computer Science at University of Bari. My supervisor is Prof. Giovanni Semeraro.
- APRIL 2015 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Semantic Query Suggestion: a probabilistic query auto-completion model based on dependencies between concepts″.
- MARCH 2005 - I received a Bachelor's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Personalized Relevance Feedback: a technique to introduce the user preferences into a information retrieval model″.
- LxMLS 2016. Lisbon Machine Learning School - Lisbon, Portugal
- RegML 2016. Regularization Methods for Machine Learning - Genoa, Italy
- G. Rossiello. Neural Abstractive Text Summarization. Doctoral Consortium at International Conference of the Italian Association for Artificial Intelligence. AI*IA 2016.
- G. Rossiello, P. Basile, G. Semeraro, M. Di Ciano and G. Grasso. Improving Neural Abstractive Text Summarization with Prior Knowledge. 1st Workshop on Deep Understanding and Reasoning: A challenge for Next-generation Intelligent Agents. URANIA 2016.
- C. Greco, A. Suglia, P. Basile, G. Rossiello and G. Semeraro. Iterative Multi-document Neural Attention for Multiple Answer Prediction. 1st Workshop on Deep Understanding and Reasoning: A challenge for Next-generation Intelligent Agents. URANIA 2016.
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems. NLDB 2016
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. SEPIR: a SEmantic and Personalised Information Retrieval Tool for the Public Administration based on Distributional Semantics. International Journal of Electronic Governance (IJEG)
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016
- A. Caputo, P. Basile, M. de Gemmis, P. Lops, G. Semeraro and G. Rossiello. SABRE: a Sentiment Aspect-Based Retrieval Engine. Information Filtering and Retrieval. DART 2014: Revised and Invited Papers.
- AI*IA 2016. The 15th International Conference of the Italian Association for Artificial Intelligence. 29 November 2016 - 1 December, Genoa (IT)
- URANIA 2016. 1st Workshop on Deep Understanding and Reasoning: A challenge for Next-generation Intelligent Agents, AI*IA 2016. 28 November 2016, Genoa (IT)
- NLDB 2016. 21st International conference on the Application of Natural Language to Information Systems. 22-24 June 2016 at the University of Salford, MediaCityUK Campus, Manchester (UK).
- FINREC 2016. 2nd International Workshop on Personalization and Recommender Systems in Financial Services. 16 June 2016, Bari (IT).
- PROMETHEUS - Prometheus is a tool for auto-completion where the queries are dynamically generated from a document collection. It is based on a Probabilistic Graphical Model, called Factor Graph, able to model the dependencies between concepts. It is useful in contexts (eg. enterprise environments) where query logs are not available.
http://i.imgur.com/0n9fJnx.jpg
Gaetano Rossiello
PhD Student
University of Bari "Aldo Moro"
Department of Computer Science
Via E.Orabona, 4 - 70126 BARI, Italy
Phone: +39 080 5442298
e-mail: gaetano.rossiello[_AT_]uniba.it
Research Interests | Short CV | Publications | Tools
Main Research Interests
- Natural Language Processing
- Information Retrieval
- Recommender System
- Machine Learning
- Deep Learning
Short CV
- NOVEMBER 2015 - Start the Ph.D. in Computer Science at University of Bari. My supervisor is Prof. Giovanni Semeraro.
- APRIL 2015 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Semantic Query Suggestion: a probabilistic query auto-completion model based on dependencies between concepts″.
- MARCH 2005 - I received a Bachelor's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Personalized Relevance Feedback: a technique to introduce the user preferences into a information retrieval model″.
Publications
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems. NLDB 2016
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016
- A. Caputo, P. Basile, M. de Gemmis, P. Lops, G. Semeraro and G. Rossiello. SABRE: a Sentiment Aspect-Based Retrieval Engine. Information Filtering and Retrieval. DART 2014: Revised and Invited Papers. Cristian Lai, Alessandro Giuliani and Giovanni Semeraro (eds.). Springer Verlag in the series Studies in Computational Intelligence (Series Ed.: Kacprzyk, Janusz, ISSN: 1860-949X)
Tools
- PROMETHEUS - Prometheus is a tool for auto-completion where the queries are dynamically generated from a document collection. It is based on a Probabilistic Graphical Model, called Factor Graph, able to model the dependencies between concepts. It is useful in contexts (eg. enterprise environments) where query logs are not available.
- REDIRECT Membri.GaetanoRossiello
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems. NLDB 2016.
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016.
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems. NLDB 2016
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems (NLDB 2016)
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems. NLDB 2016.
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016.
- P. Basile, A. Caputo, G. Rossiello and G. Semeraro. Learning to Rank Entity Relatedness through Embedding-Based Features. Proceedings of the 21st International conference on the Application of Natural Language to Information Systems (NLDB 2016)
- P. Basile, A. Caputo, M. Di Ciano, G. Grasso, G. Rossiello and G. Semeraro. Distributional Semantics for Intelligent Information Access of Documents within Public Administration. I-Cities 2016
- PROMETHEUS - Prometheus is a tool for auto-completion of queries automatically extracted from the corpus. It is based on a probabilistic graphical model, called Factor Graph, able to model the dependencies between concepts. It is useful in contexts (eg. enterprise environments) where query logs are not available.
- PROMETHEUS - Prometheus is a tool for auto-completion where the queries are dynamically generated from a document collection. It is based on a Probabilistic Graphical Model, called Factor Graph, able to model the dependencies between concepts. It is useful in contexts (eg. enterprise environments) where query logs are not available.
Text Mining
- Distributional Semantics
- Entity Linking & Recommendation
Information Retrieval
- IR Models
- Query Auto-Completion
- Query Expansion
- Query Recommendation
Machine Learning
- Learning to Rank
- Ensemble Learning
- Information Retrieval
- Recommender System
- Machine Learning
- A. Caputo, P. Basile, M. de Gemmis, P. Lops, G. Semeraro and G. Rossiello SABRE: a Sentiment Aspect-Based Retrieval Engine. Information Filtering and Retrieval. DART 2014: Revised and Invited Papers. Cristian Lai, Alessandro Giuliani and Giovanni Semeraro (eds.).
Springer Verlag in the series Studies in Computational Intelligence (Series Ed.: Kacprzyk, Janusz, ISSN: 1860-949X)
- A. Caputo, P. Basile, M. de Gemmis, P. Lops, G. Semeraro and G. Rossiello. SABRE: a Sentiment Aspect-Based Retrieval Engine. Information Filtering and Retrieval. DART 2014: Revised and Invited Papers. Cristian Lai, Alessandro Giuliani and Giovanni Semeraro (eds.). Springer Verlag in the series Studies in Computational Intelligence (Series Ed.: Kacprzyk, Janusz, ISSN: 1860-949X)
Publications
- A. Caputo, P. Basile, M. de Gemmis, P. Lops, G. Semeraro and G. Rossiello SABRE: a Sentiment Aspect-Based Retrieval Engine. Information Filtering and Retrieval. DART 2014: Revised and Invited Papers. Cristian Lai, Alessandro Giuliani and Giovanni Semeraro (eds.).
Springer Verlag in the series Studies in Computational Intelligence (Series Ed.: Kacprzyk, Janusz, ISSN: 1860-949X)
Short CV
- NOVEMBER 2015 - Start the Ph.D. in Computer Science at University of Bari. My supervisor is Prof. Giovanni Semeraro.
- APRIL 2015 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Semantic Query Suggestion: a probabilistic query auto-completion model based on dependencies between concepts″.
- MARCH 2005 - I received a Bachelor's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Personalized Relevance Feedback: a technique to introduce the user preferences into a information retrieval model″.
Tools
- PROMETHEUS - Prometheus is a tool for auto-completion of queries automatically extracted from the corpus. It is based on a probabilistic graphical model, called Factor Graph, able to model the dependencies between concepts. It is useful in contexts (eg. enterprise environments) where query logs are not available.
Semantic Web
- Knowledge Representation
- Ontologies
- Linked Open Data
Machine Learning
- Learning to Rank
- Ensemble Learning
- Deep Learning
- Entity Linking & Recommentation
- Entity Linking & Recommendation
- Entity Linking
- Entity Recommentation
- Entity Linking & Recommentation
Semantic Web
- Knowledge Representation
- Ontologies
- Linked Open Data
Text Mining
- Natural Language Processing
- Entity Linking
- Entity Recommentation
- Distributional Semantics
- Recommender Systems
- Information Filtering
- Hybrid Recommendation Strategies
- Machine Learning Techniques for Recommender Systems
Personalization
- Emotions detection and extraction
- Personality as user profile feature
- Weak sentiment Analysis and features extraction
- User Modeling and Profiling
- IR Models
- Query Auto-Completion
- Query Expansion
- Query Recommendation
Main Research Interests
Information Retrieval
- Recommender Systems
- Information Filtering
- Hybrid Recommendation Strategies
- Machine Learning Techniques for Recommender Systems
Personalization
- Emotions detection and extraction
- Personality as user profile feature
- Weak sentiment Analysis and features extraction
- User Modeling and Profiling
http://i.imgur.com/0n9fJnx.jpg
Gaetano Rossiello
PhD Student
University of Bari "Aldo Moro"
Department of Computer Science
Via E.Orabona, 4 - 70126 BARI, Italy
Phone: +39 080 5442298
e-mail: gaetano.rossiello[_AT_]uniba.it