AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2020
Trifir`o, Irene; Augello, Agnese; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo; Meo, Rosa
How Are You? How a Robot Can Learn to Express Its Own Roboceptions Proceedings Article
In: Cristiani, Matteo; Toro, Carlos; Zanni-Merk, Cecilia; Howlett, Robert J.; Jain, Lakhmi C. (Ed.): Procedia Computer Science, pp. 480–489, Elsevier B.V., 2020.
Abstract | Links | BibTeX | Tags: Human computer interaction, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Robotics, Semantic Computing, Social Robots
@inproceedings{trifiroHowAreYou2020,
title = {How Are You? How a Robot Can Learn to Express Its Own Roboceptions},
author = { Irene Trifir{`o} and Agnese Augello and Umberto Maniscalco and Giovanni Pilato and Filippo Vella and Rosa Meo},
editor = { Matteo Cristiani and Carlos Toro and Cecilia {Zanni-Merk} and Robert J. Howlett and Lakhmi C. Jain},
doi = {10.1016/j.procs.2020.08.050},
year = {2020},
date = {2020-01-01},
booktitle = {Procedia Computer Science},
volume = {176},
pages = {480--489},
publisher = {Elsevier B.V.},
abstract = {This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named ``roboceptions''. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.},
keywords = {Human computer interaction, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Robotics, Semantic Computing, Social Robots},
pubstate = {published},
tppubtype = {inproceedings}
}
This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named ``roboceptions''. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.
2016
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni; Vassallo, Giorgio
Semantic Word Error Rate for Sentence Similarity Proceedings Article
In: Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016, pp. 266–269, Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 978-1-5090-0661-8.
Abstract | Links | BibTeX | Tags: Human computer interaction, Latent Semantic Analysis, Natural Language Processing, Semantic Computing
@inproceedings{spicciaSemanticWordError2016,
title = {Semantic Word Error Rate for Sentence Similarity},
author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato and Giorgio Vassallo},
doi = {10.1109/ICSC.2016.11},
isbn = {978-1-5090-0661-8},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016},
pages = {266--269},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Sentence similarity measures have applications in several tasks, including: Machine Translation, Paraphrase Identification, Speech Recognition, Question-answering and Text Summarization. However, measures designed for these tasks are aimed at assessing equivalence rather than resemblance, partly departing from human cognition of similarity. While this is reasonable for these activities, it hinders the applicability of sentence similarity measures to other tasks. We therefore propose a new sentence similarity measure specifically designed for resemblance evaluation, in order to cover these fields better. Experimental results are discussed. textcopyright 2016 IEEE.},
keywords = {Human computer interaction, Latent Semantic Analysis, Natural Language Processing, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Sentence similarity measures have applications in several tasks, including: Machine Translation, Paraphrase Identification, Speech Recognition, Question-answering and Text Summarization. However, measures designed for these tasks are aimed at assessing equivalence rather than resemblance, partly departing from human cognition of similarity. While this is reasonable for these activities, it hinders the applicability of sentence similarity measures to other tasks. We therefore propose a new sentence similarity measure specifically designed for resemblance evaluation, in order to cover these fields better. Experimental results are discussed. textcopyright 2016 IEEE.
2015
Ditta, Marilena; Milazzo, Fabrizio; Raví, Valentina; Pilato, Giovanni; Augello, Agnese
Data-Driven Relation Discovery from Unstructured Texts Proceedings Article
In: A., Filipe J. Liu K. Aveiro D. Dietz J. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 597–602, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | BibTeX | Tags: Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing
@inproceedings{dittaDatadrivenRelationDiscovery2015,
title = {Data-Driven Relation Discovery from Unstructured Texts},
author = { Marilena Ditta and Fabrizio Milazzo and Valentina Raví and Giovanni Pilato and Agnese Augello},
editor = { Filipe J. Liu K. Aveiro D. Dietz J. Filipe J. Fred A.},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {597--602},
publisher = {SciTePress},
abstract = {This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni; Vassallo, Giorgio
A Word Prediction Methodology for Automatic Sentence Completion Proceedings Article
In: M.S., Li T. Wang W. Kankanhalli (Ed.): Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015, pp. 240–243, Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 978-1-4799-7935-6.
Abstract | Links | BibTeX | Tags: Computational Linguistics, Language Model, Latent Semantic Analysis, Semantic Computing, Semantic Spaces
@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}
}
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.
2013
Augello, Agnese; Gaglio, Salvatore; Pilato, Giovanni; Vassallo, Giorgio
Clifford Rotors for Conceptual Representation in Chatbots Journal Article
In: Advances in Intelligent Systems and Computing, vol. 196 AISC, pp. 369–370, 2013, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Chatbots, Clifford algebra, Conceptual Spaces, Geometric algebra, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Semantic Computing
@article{augelloCliffordRotorsConceptual2013,
title = {Clifford Rotors for Conceptual Representation in Chatbots},
author = { Agnese Augello and Salvatore Gaglio and Giovanni Pilato and Giorgio Vassallo},
doi = {10.1007/978-3-642-34274-5_64},
issn = {21945357},
year = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {196 AISC},
pages = {369--370},
abstract = {In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot. The chatbot KB is coded in a conceptual space induced from the application of the Latent Semantic Analysis (LSA) paradigm on a corpus of documents. LSA has the effect of decomposing the original relationships between elements into linearly-independent vectors. Each basis vector can be considered therefore as a "conceptual coordinate", which can be tagged by the words which better characterize it. This tagging is obtained by performing a (TF-IDF)-like weighting schema [3], that we call TW-ICW (term weight-inverse conceptual coordinate weight), to weigh the relevance of each term on each conceptual coordinate. textcopyright 2013 Springer-Verlag.},
keywords = {Chatbots, Clifford algebra, Conceptual Spaces, Geometric algebra, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot. The chatbot KB is coded in a conceptual space induced from the application of the Latent Semantic Analysis (LSA) paradigm on a corpus of documents. LSA has the effect of decomposing the original relationships between elements into linearly-independent vectors. Each basis vector can be considered therefore as a "conceptual coordinate", which can be tagged by the words which better characterize it. This tagging is obtained by performing a (TF-IDF)-like weighting schema [3], that we call TW-ICW (term weight-inverse conceptual coordinate weight), to weigh the relevance of each term on each conceptual coordinate. textcopyright 2013 Springer-Verlag.
2008
Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore; Pilato, Giovanni
Sentence Induced Transformations in "Conceptual" Spaces Proceedings Article
In: Proceedings - IEEE International Conference on Semantic Computing 2008, ICSC 2008, pp. 34–41, 2008, ISBN: 978-0-7695-3279-0.
Abstract | Links | BibTeX | Tags: Clifford algebra, Geometric algebra, Latent Semantic Analysis, Natural Language Processing, Semantic Computing
@inproceedings{augelloSentenceInducedTransformations2008,
title = {Sentence Induced Transformations in "Conceptual" Spaces},
author = { Agnese Augello and Giorgio Vassallo and Salvatore Gaglio and Giovanni Pilato},
doi = {10.1109/ICSC.2008.74},
isbn = {978-0-7695-3279-0},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings - IEEE International Conference on Semantic Computing 2008, ICSC 2008},
pages = {34--41},
abstract = {The proposed work illustrates how "primitive concepts "can be automatically induced from a text corpus. The primitive concepts are identified by the orthonormal axis of a "conceptual" space induced by a methodology inspired to the Latent Semantic Analysis approach. The methodology represents a natural language sentence by means of a set of rotations of an orthonormal basis in the "conceptual" space. The rotations, triggered by the sequence of words composing the sentence and realized by means of Geometric Algebra rotors, allow to highlight "conceptual" relations that can arise among the primitive concepts. textcopyright 2008 IEEE.},
keywords = {Clifford algebra, Geometric algebra, Latent Semantic Analysis, Natural Language Processing, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
The proposed work illustrates how "primitive concepts "can be automatically induced from a text corpus. The primitive concepts are identified by the orthonormal axis of a "conceptual" space induced by a methodology inspired to the Latent Semantic Analysis approach. The methodology represents a natural language sentence by means of a set of rotations of an orthonormal basis in the "conceptual" space. The rotations, triggered by the sequence of words composing the sentence and realized by means of Geometric Algebra rotors, allow to highlight "conceptual" relations that can arise among the primitive concepts. textcopyright 2008 IEEE.
Pilato, Giovanni; Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore
Sub-Symbolic Knowledge Representation for Evocative Chat-Bots Book
Physica-Verlag HD, 2008, ISBN: 978-3-7908-2009-6.
Abstract | Links | BibTeX | Tags: Chatbots, Knowledge Representation, Latent Semantic Analysis
@book{pilatoSubsymbolicKnowledgeRepresentation2008,
title = {Sub-Symbolic Knowledge Representation for Evocative Chat-Bots},
author = { Giovanni Pilato and Agnese Augello and Giorgio Vassallo and Salvatore Gaglio},
doi = {10.1007/978-3-7908-2010-2_42},
isbn = {978-3-7908-2009-6},
year = {2008},
date = {2008-01-01},
publisher = {Physica-Verlag HD},
abstract = {A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with evocative capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE architecture. Experimental trials involving four lexical categories of WordNet have been conducted, and an example of interaction is shown at the end of the paper. textcopyright 2008 Physica-Verlag Heidelberg.},
keywords = {Chatbots, Knowledge Representation, Latent Semantic Analysis},
pubstate = {published},
tppubtype = {book}
}
A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with evocative capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE architecture. Experimental trials involving four lexical categories of WordNet have been conducted, and an example of interaction is shown at the end of the paper. textcopyright 2008 Physica-Verlag Heidelberg.
2007
Pilato, Giovanni; Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore
Geometrie Algebra Rotors for Sub-Symbolic Coding of Natural Language Sentences Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4692 LNAI, no. PART 1, pp. 42–51, 2007, ISSN: 03029743.
Abstract | BibTeX | Tags: Clifford algebra, Geometric algebra, Latent Semantic Analysis, Natural Language Processing
@article{pilatoGeometrieAlgebraRotors2007,
title = {Geometrie Algebra Rotors for Sub-Symbolic Coding of Natural Language Sentences},
author = { Giovanni Pilato and Agnese Augello and Giorgio Vassallo and Salvatore Gaglio},
issn = {03029743},
year = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4692 LNAI},
number = {PART 1},
pages = {42--51},
abstract = {A sub-symbolic encoding methodology for natural language sentences is presented. The procedure is based on the creation of an LSA-inspired semantic space and associates rotation operators derived from Geometric Algebra to word bigrams of the sentence. The operators are subsequently applied to an orthonormal standard basis of the created semantic space according to the order in which words appear in the sentence. The final rotated basis is then coded as a vector and its orthogonal part constitutes the sub-symbolic coding of the sentence. Preliminary experimental results for a classification task, compared with the traditional LSA methodology, show the effectiveness of the approach. textcopyright Springer-Verlag Berlin Heidelberg 2007.},
keywords = {Clifford algebra, Geometric algebra, Latent Semantic Analysis, Natural Language Processing},
pubstate = {published},
tppubtype = {article}
}
A sub-symbolic encoding methodology for natural language sentences is presented. The procedure is based on the creation of an LSA-inspired semantic space and associates rotation operators derived from Geometric Algebra to word bigrams of the sentence. The operators are subsequently applied to an orthonormal standard basis of the created semantic space according to the order in which words appear in the sentence. The final rotated basis is then coded as a vector and its orthogonal part constitutes the sub-symbolic coding of the sentence. Preliminary experimental results for a classification task, compared with the traditional LSA methodology, show the effectiveness of the approach. textcopyright Springer-Verlag Berlin Heidelberg 2007.
Pilato, Giovanni; Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore
Sub-Symbolic Semantic Layer in Cyc for Intuitive Chat-Bots Proceedings Article
In: ICSC 2007 International Conference on Semantic Computing, pp. 121–128, 2007, ISBN: 0-7695-2997-6 978-0-7695-2997-4.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Chatbots, Common Sense Reasoning, Conversational Agents, Cyc Ontology, Knowledge Representation, Latent Semantic Analysis, Ontologies, Semantic Computing, Semantic Spaces
@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}
}
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.
2005
Pilato, Giovanni; Vassallo, Giorgio; Vasile, Maria; Augello, Agnese; Gaglio, Salvatore
A Simple Solution for Improving the Effectiveness of Traditional Information Retrieval Systems Proceedings Article
In: Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 2005, ISBN: 960-8457-09-2.
Abstract | BibTeX | Tags: Human computer interaction, Information Retrieval, Latent Semantic Analysis, Semantic Spaces
@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}
}
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.