Publications

@inproceedings{spicciaWordPredictionMethodology2015,

title = {A Word Prediction Methodology for Automatic Sentence Completion},

author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato and Giorgio Vassallo},

editor = { Li T. Wang W. Kankanhalli M.S.},

doi = {10.1109/ICOSC.2015.7050813},

isbn = {978-1-4799-7935-6},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015},

pages = {240--243},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network language models. textcopyright 2015 IEEE.},

keywords = {Computational Linguistics, Language Model, Latent Semantic Analysis, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloGeometricAlgebraBased2014,

title = {A Geometric Algebra Based Distributional Model to Encode Sentences Semantics},

author = { Agnese Augello and Manuel Gentile and Giovanni Pilato and Giorgio Vassallo},

editor = { C Lai and A Giuliani and G Semeraro},

doi = {10.1007/978-3-642-40621-8_6},

isbn = {978-3-642-40620-1},

year  = {2014},

date = {2014-01-01},

booktitle = {DISTRIBUTED SYSTEMS AND APPLICATIONS OF INFORMATION FILTERING AND RETRIEVAL: DART 2012: REVISED AND INVITED PAPERS},

volume = {515},

pages = {101--114},

publisher = {SPRINGER-VERLAG BERLIN},

address = {HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY},

series = {Studies in Computational Intelligence},

abstract = {Word space models are used to encode the semantics of natural language elements by means of high dimensional vectors [23]. Latent Semantic Analysis (LSA) methodology [15] is well known and widely used for its generalization properties. Despite of its good performance in several applications, the model induced by LSA ignores dynamic changes in sentences meaning that depend on the order of the words, because it is based on a bag of words analysis. In this chapter we present a technique that exploits LSA-based semantic spaces and geometric algebra in order to obtain a sub-symbolic encoding of sentences taking into account the words sequence in the sentence.},

keywords = {Natural Language Processing, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloGeometricEncodingSentences2012,

title = {Geometric Encoding of Sentences Based on Clifford Algebra},

author = { Agnese Augello and Manuel Gentile and Giovanni Pilato and Giorgio Vassallo},

isbn = {978-989-8565-29-7},

year  = {2012},

date = {2012-01-01},

booktitle = {KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval},

pages = {457--462},

abstract = {Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright textcopyright 2012 SciTePress - Science and Technology Publications.},

keywords = {Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloModularFrameworkVersatile2011,

title = {A Modular Framework for Versatile Conversational Agent Building},

author = { Agnese Augello and Mario Scriminaci and Salvatore Gaglio and Giovanni Pilato},

doi = {10.1109/CISIS.2011.95},

isbn = {978-0-7695-4373-4},

year  = {2011},

date = {2011-01-01},

booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011},

pages = {577--582},

abstract = {This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. textcopyright 2011 IEEE.},

keywords = {Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloSemanticLayerSemistructured2009,

title = {A Semantic Layer on Semi-Structured Data Sources for Intuitive Chatbots},

author = { Agnese Augello and Giorgio Vassallo and Salvatore Gaglio and Giovanni Pilato},

doi = {10.1109/CISIS.2009.165},

isbn = {978-0-7695-3575-3},

year  = {2009},

date = {2009-01-01},

booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009},

pages = {760--765},

abstract = {The main limits of chatbot technology are related to the building of their knowledge representation and to their rigid information retrieval and dialogue capabilities, usually based on simple "pattern matching rules". The analysis of distributional properties of words in a texts corpus allows the creation of semantic spaces where represent and compare natural language elements. This space can be interpreted as a "conceptual" space where the axes represent the latent primitive concepts of the analyzed corpus. The presented work aims at exploiting the properties of a data-driven semantic/conceptual space built using semistructured data sources freely available on the web, like Wikipedia. This coding is equivalent to adding, into the Wikipedia graph, a conceptual similarity relationship layer. The chatbot can exploit this layer in order to simulate an "intuitive" behavior, attempting to retrieve semantic relations between Wikipedia resources also through associative sub-symbolic paths. textcopyright 2009 IEEE.},

keywords = {Chatbots, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pilatoSubsymbolicSemanticLayer2007,

title = {Sub-Symbolic Semantic Layer in Cyc for Intuitive Chat-Bots},

author = { Giovanni Pilato and Agnese Augello and Giorgio Vassallo and Salvatore Gaglio},

doi = {10.1109/ICSC.2007.37},

isbn = {0-7695-2997-6 978-0-7695-2997-4},

year  = {2007},

date = {2007-01-01},

booktitle = {ICSC 2007 International Conference on Semantic Computing},

pages = {121--128},

abstract = {The work presented in this paper aims to combine Latent Semantic Analysis methodology, common sense and traditional knowledge representation in order to improve the dialogue capabilities of a conversational agent. In our approach the agent brain is characterized by two areas: a "rational area", composed by a structured, rule-based knowledge base, and an "associative area", obtained through a data-driven semantic space. Concepts are mapped in this space and their mutual geometric distance is related to their conceptual similarity. The geometric distance between concepts implicitly defines a sub-symbolic relationship net, which can be seen as a new "subsymbolic semantic layer" automatically added to the Cyc ontology. Users queries can also be mapped in the same conceptual space, and evoke similar ontology concepts. As a result the agent can exploit this feature, attempting to retrieve ontological concepts that are not easily reachable by means of the traditional ontology reasoning engine. textcopyright 2007 IEEE.},

keywords = {Artificial intelligence, Chatbots, Common Sense Reasoning, Conversational Agents, Cyc Ontology, Knowledge Representation, Latent Semantic Analysis, Ontologies, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pilatoSimpleSolutionImproving2005,

title = {A Simple Solution for Improving the Effectiveness of Traditional Information Retrieval Systems},

author = { Giovanni Pilato and Giorgio Vassallo and Maria Vasile and Agnese Augello and Salvatore Gaglio},

isbn = {960-8457-09-2},

year  = {2005},

date = {2005-01-01},

booktitle = {Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems},

publisher = {World Scientific and Engineering Academy and Society (WSEAS)},

address = {Stevens Point, Wisconsin, USA},

series = {SEPADS'05},

abstract = {In this paper we present a system based on the LSA paradigm to improve the performance of a traditional information retrieval system. The proposed system aims to improve both the recall and the precision capabilities of traditional search engines thanks to a semantic query expansion and a subsequent semantic results filtering. A collection of 650 documents has been used to compare the performances of the proposed system with a traditional search engine. Experimental trials show the effectiveness of the proposed solution.},

keywords = {Human computer interaction, Information Retrieval, Latent Semantic Analysis, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}