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.
2022
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Vitale, Gianpaolo
Extending Affective Capabilities for Medical Assistive Robots Journal Article
In: Cognitive Systems Research, vol. 73, pp. 21–25, 2022, ISSN: 13890417.
Abstract | Links | BibTeX | Tags: Anthropomorphic Robots, Assistive Robots, Emotion Detection, Facial Expressions, Human computer interaction, Human Robot Interaction, Humanoid Robots, Natural Language Processing, Robotics, Wellbeing
@article{augelloExtendingAffectiveCapabilities2022,
title = {Extending Affective Capabilities for Medical Assistive Robots},
author = { Agnese Augello and Ignazio Infantino and Giovanni Pilato and Gianpaolo Vitale},
doi = {10.1016/j.cogsys.2021.12.004},
issn = {13890417},
year = {2022},
date = {2022-01-01},
journal = {Cognitive Systems Research},
volume = {73},
pages = {21--25},
abstract = {In this work, we discuss methodologies and implementation choices to enable a humanoid robot to estimate patients' mood and emotions during postoperative home rehabilitation. The approach is modular and it has been implemented into a SoftBank Pepper robotic architecture; however, the approach is general and it can be easily adapted to other robotic platforms. A sample of an interactive session for the detection of the patient's affective state is also reported. textcopyright 2022 Elsevier B.V.},
keywords = {Anthropomorphic Robots, Assistive Robots, Emotion Detection, Facial Expressions, Human computer interaction, Human Robot Interaction, Humanoid Robots, Natural Language Processing, Robotics, Wellbeing},
pubstate = {published},
tppubtype = {article}
}
In this work, we discuss methodologies and implementation choices to enable a humanoid robot to estimate patients' mood and emotions during postoperative home rehabilitation. The approach is modular and it has been implemented into a SoftBank Pepper robotic architecture; however, the approach is general and it can be easily adapted to other robotic platforms. A sample of an interactive session for the detection of the patient's affective state is also reported. textcopyright 2022 Elsevier B.V.
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Vitale, Gianpaolo
Extending affective capabilities for medical assistive robots Journal Article
In: Cognitive Systems Research, vol. 73, pp. 21–25, 2022, ISSN: 13890417.
Abstract | Links | BibTeX | Tags: Anthropomorphic Robots, Assistive Robots, Emotion Detection, Facial Expressions, Human computer interaction, Human Robot Interaction, Humanoid Robots, Natural Language Processing, Robotics, Wellbeing
@article{augello_extending_2022,
title = {Extending affective capabilities for medical assistive robots},
author = {Agnese Augello and Ignazio Infantino and Giovanni Pilato and Gianpaolo Vitale},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123046436&doi=10.1016%2fj.cogsys.2021.12.004&partnerID=40&md5=6e76332f7f95333a9ae2e8f11c054622},
doi = {10.1016/j.cogsys.2021.12.004},
issn = {13890417},
year = {2022},
date = {2022-01-01},
journal = {Cognitive Systems Research},
volume = {73},
pages = {21–25},
abstract = {In this work, we discuss methodologies and implementation choices to enable a humanoid robot to estimate patients’ mood and emotions during postoperative home rehabilitation. The approach is modular and it has been implemented into a SoftBank Pepper robotic architecture; however, the approach is general and it can be easily adapted to other robotic platforms. A sample of an interactive session for the detection of the patient's affective state is also reported. © 2022 Elsevier B.V.},
keywords = {Anthropomorphic Robots, Assistive Robots, Emotion Detection, Facial Expressions, Human computer interaction, Human Robot Interaction, Humanoid Robots, Natural Language Processing, Robotics, Wellbeing},
pubstate = {published},
tppubtype = {article}
}
In this work, we discuss methodologies and implementation choices to enable a humanoid robot to estimate patients’ mood and emotions during postoperative home rehabilitation. The approach is modular and it has been implemented into a SoftBank Pepper robotic architecture; however, the approach is general and it can be easily adapted to other robotic platforms. A sample of an interactive session for the detection of the patient's affective state is also reported. © 2022 Elsevier B.V.
2021
Augello, Agnese; Gentile, Manuel; Picone, Marco
Mind Games Proceedings Article
In: pp. 84–93, 2021.
Abstract | BibTeX | Tags: Education, Mental Maps, Natural Language Processing, Serious game
@inproceedings{augelloMindGames2021,
title = {Mind Games},
author = { Agnese Augello and Manuel Gentile and Marco Picone},
year = {2021},
date = {2021-01-01},
pages = {84--93},
abstract = {This paper describes the preliminary stages and the theoretical and methodological inspiration for the design of UrbanCraft, a gamified activity based on Kevin Lynch's mental maps and aiming at enhancing youth participation and inclusion in urban co-design and governance. This activity draws on previous local experiences on participation and social inclusion, held in the Southern Italian city of Palermo, in an attempt to mix urban studies, serious games and artificial intelligence methodologies. In this experiment, we designed a gamified activity conceived for nine- to twelve-year-old primary and secondary school children, due to our previous cooperation with several primary and secondary schools in Palermo. Overall, the goal of this proposal is to adapt Lynch's notions of imageability and public images to a serious game and lead the players to an enhanced spatial awareness of their neighborhood and the entire city. Although UrbanCraft is still in its early design stages, the existing network of public institutions, research centers and schools provides a fertile ground for developing the project.},
keywords = {Education, Mental Maps, Natural Language Processing, Serious game},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes the preliminary stages and the theoretical and methodological inspiration for the design of UrbanCraft, a gamified activity based on Kevin Lynch's mental maps and aiming at enhancing youth participation and inclusion in urban co-design and governance. This activity draws on previous local experiences on participation and social inclusion, held in the Southern Italian city of Palermo, in an attempt to mix urban studies, serious games and artificial intelligence methodologies. In this experiment, we designed a gamified activity conceived for nine- to twelve-year-old primary and secondary school children, due to our previous cooperation with several primary and secondary schools in Palermo. Overall, the goal of this proposal is to adapt Lynch's notions of imageability and public images to a serious game and lead the players to an enhanced spatial awareness of their neighborhood and the entire city. Although UrbanCraft is still in its early design stages, the existing network of public institutions, research centers and schools provides a fertile ground for developing the project.
Augello, Agnese; Gentile, Manuel; Picone, Marco
Mind Games Proceedings Article
In: pp. 84–93, 2021.
Abstract | BibTeX | Tags: Education, Mental Maps, Natural Language Processing, Serious game
@inproceedings{augello_mind_2021,
title = {Mind Games},
author = {Agnese Augello and Manuel Gentile and Marco Picone},
year = {2021},
date = {2021-01-01},
pages = {84–93},
abstract = {This paper describes the preliminary stages and the theoretical and methodological inspiration for the design of UrbanCraft, a gamified activity based on Kevin Lynchś mental maps and aiming at enhancing youth participation and inclusion in urban co-design and governance. This activity draws on previous local experiences on participation and social inclusion, held in the Southern Italian city of Palermo, in an attempt to mix urban studies, serious games and artificial intelligence methodologies. In this experiment, we designed a gamified activity conceived for nine- to twelve-year-old primary and secondary school children, due to our previous cooperation with several primary and secondary schools in Palermo. Overall, the goal of this proposal is to adapt Lynchś notions of imageability and public images to a serious game and lead the players to an enhanced spatial awareness of their neighborhood and the entire city. Although UrbanCraft is still in its early design stages, the existing network of public institutions, research centers and schools provides a fertile ground for developing the project.},
keywords = {Education, Mental Maps, Natural Language Processing, Serious game},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes the preliminary stages and the theoretical and methodological inspiration for the design of UrbanCraft, a gamified activity based on Kevin Lynchś mental maps and aiming at enhancing youth participation and inclusion in urban co-design and governance. This activity draws on previous local experiences on participation and social inclusion, held in the Southern Italian city of Palermo, in an attempt to mix urban studies, serious games and artificial intelligence methodologies. In this experiment, we designed a gamified activity conceived for nine- to twelve-year-old primary and secondary school children, due to our previous cooperation with several primary and secondary schools in Palermo. Overall, the goal of this proposal is to adapt Lynchś notions of imageability and public images to a serious game and lead the players to an enhanced spatial awareness of their neighborhood and the entire city. Although UrbanCraft is still in its early design stages, the existing network of public institutions, research centers and schools provides a fertile ground for developing the project.
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.
Trifirò, 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{trifiro_how_2020,
title = {How are you? How a robot can learn to express its own roboceptions},
author = {Irene Trifirò 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},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093358258&doi=10.1016%2fj.procs.2020.08.050&partnerID=40&md5=d262d3c7852f492f6a871ed2c4b7e941},
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
Augello, Agnese; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo
Disaster Prevention Virtual Advisors through Soft Sensor Paradigm Journal Article
In: Smart Innovation, Systems and Technologies, vol. 55, pp. 619–627, 2016, ISSN: 21903018.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Conversational Agents, Decision Support Systems, Disaster Prevention, Human computer interaction, Natural Language Processing, Ontologies, Sensor systems
@article{augelloDisasterPreventionVirtual2016,
title = {Disaster Prevention Virtual Advisors through Soft Sensor Paradigm},
author = { Agnese Augello and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},
editor = { Giuseppe De Pietro and Luigi Gallo and Robert J. Howlett and Lakhmi C. Jain},
doi = {10.1007/978-3-319-39345-2_55},
issn = {21903018},
year = {2016},
date = {2016-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {55},
pages = {619--627},
abstract = {In this paper we illustrate the architecture of an intelligent advisor agent aimed at limiting, or as far as possible preventing, the damages caused by catastrophic events, such as floods and landslides. The agent models the domain and makes forecasting by exploiting both ontology models and belief network models. Furthermore, it uses a monitoring network to recommend preventive measures and giving alerts, if necessary, before that the event happens. The monitoring network can be implemented through both physical and soft sensors: this choice makes the measurements more adequate and available also in case of failure of some of the physical sensors. The front-end of the agent is made by a chat-bot, capable to interact with human users using natural language. textcopyright Springer International Publishing Switzerland 2016.},
keywords = {Artificial intelligence, Conversational Agents, Decision Support Systems, Disaster Prevention, Human computer interaction, Natural Language Processing, Ontologies, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
In this paper we illustrate the architecture of an intelligent advisor agent aimed at limiting, or as far as possible preventing, the damages caused by catastrophic events, such as floods and landslides. The agent models the domain and makes forecasting by exploiting both ontology models and belief network models. Furthermore, it uses a monitoring network to recommend preventive measures and giving alerts, if necessary, before that the event happens. The monitoring network can be implemented through both physical and soft sensors: this choice makes the measurements more adequate and available also in case of failure of some of the physical sensors. The front-end of the agent is made by a chat-bot, capable to interact with human users using natural language. textcopyright Springer International Publishing Switzerland 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.
Augello, Agnese; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo
Disaster prevention virtual advisors through soft sensor paradigm Journal Article
In: Smart Innovation, Systems and Technologies, vol. 55, pp. 619–627, 2016, ISSN: 21903018.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Conversational Agents, Decision Support Systems, Disaster Prevention, Human computer interaction, Natural Language Processing, Ontologies, Sensor systems
@article{augello_disaster_2016,
title = {Disaster prevention virtual advisors through soft sensor paradigm},
author = {Agnese Augello and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},
editor = {Giuseppe De Pietro and Luigi Gallo and Robert J. Howlett and Lakhmi C. Jain},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977119923&doi=10.1007%2f978-3-319-39345-2_55&partnerID=40&md5=a54a699ddea48ce7e9ab353ef5ce4ba0},
doi = {10.1007/978-3-319-39345-2_55},
issn = {21903018},
year = {2016},
date = {2016-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {55},
pages = {619–627},
abstract = {In this paper we illustrate the architecture of an intelligent advisor agent aimed at limiting, or as far as possible preventing, the damages caused by catastrophic events, such as floods and landslides. The agent models the domain and makes forecasting by exploiting both ontology models and belief network models. Furthermore, it uses a monitoring network to recommend preventive measures and giving alerts, if necessary, before that the event happens. The monitoring network can be implemented through both physical and soft sensors: this choice makes the measurements more adequate and available also in case of failure of some of the physical sensors. The front-end of the agent is made by a chat-bot, capable to interact with human users using natural language. © Springer International Publishing Switzerland 2016.},
keywords = {Artificial intelligence, Conversational Agents, Decision Support Systems, Disaster Prevention, Human computer interaction, Natural Language Processing, Ontologies, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
In this paper we illustrate the architecture of an intelligent advisor agent aimed at limiting, or as far as possible preventing, the damages caused by catastrophic events, such as floods and landslides. The agent models the domain and makes forecasting by exploiting both ontology models and belief network models. Furthermore, it uses a monitoring network to recommend preventive measures and giving alerts, if necessary, before that the event happens. The monitoring network can be implemented through both physical and soft sensors: this choice makes the measurements more adequate and available also in case of failure of some of the physical sensors. The front-end of the agent is made by a chat-bot, capable to interact with human users using natural language. © Springer International Publishing Switzerland 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{spiccia_semantic_2016,
title = {Semantic Word Error Rate for Sentence Similarity},
author = {Carmelo Spiccia and Agnese Augello and Giovanni Pilato and Giorgio Vassallo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968779315&doi=10.1109%2fICSC.2016.11&partnerID=40&md5=201fee836e22137835d97529488309ca},
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. © 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. © 2016 IEEE.
2015
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Rizzo, Riccardo; Vella, Filippo
Creativity Evaluation in a Cognitive Architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 11, pp. 29–37, 2015, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Cognitive Architectures, Computational Creativity, Creative Process, Creativity Evaluation, Natural Language Processing
@article{augelloCreativityEvaluationCognitive2015,
title = {Creativity Evaluation in a Cognitive Architecture},
author = { Agnese Augello and Ignazio Infantino and Giovanni Pilato and Riccardo Rizzo and Filippo Vella},
doi = {10.1016/j.bica.2014.11.013},
issn = {2212683X},
year = {2015},
date = {2015-01-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {11},
pages = {29--37},
abstract = {Evaluation is a key factor of creativity: for this reason it should be integrated into a cognitive architecture of a creative artificial agent. The approach illustrated in this paper uses the Psi model, and describes the framework for introducing internal and external evaluations, and how they influence demands and motivation of the artificial agent. Internal evaluation mechanisms drive the creative process, and influence competence of the creative agent. External evaluation acts through certainty, and requires interaction with human users that express both opinions and some subjective quantitative evaluations on the final artwork. The system uses natural language processing techniques in order to infer the satisfaction and the emotional impact of the final product obtained by the creative agent. textcopyright 2014 Elsevier B.V. All rights reserved.},
keywords = {Artificial intelligence, Cognitive Architectures, Computational Creativity, Creative Process, Creativity Evaluation, Natural Language Processing},
pubstate = {published},
tppubtype = {article}
}
Evaluation is a key factor of creativity: for this reason it should be integrated into a cognitive architecture of a creative artificial agent. The approach illustrated in this paper uses the Psi model, and describes the framework for introducing internal and external evaluations, and how they influence demands and motivation of the artificial agent. Internal evaluation mechanisms drive the creative process, and influence competence of the creative agent. External evaluation acts through certainty, and requires interaction with human users that express both opinions and some subjective quantitative evaluations on the final artwork. The system uses natural language processing techniques in order to infer the satisfaction and the emotional impact of the final product obtained by the creative agent. textcopyright 2014 Elsevier B.V. All rights reserved.
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Rizzo, Riccardo; Vella, Filippo
Creativity evaluation in a cognitive architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 11, pp. 29–37, 2015, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Cognitive Architectures, Computational Creativity, Creative Process, Creativity Evaluation, Natural Language Processing
@article{augello_creativity_2015,
title = {Creativity evaluation in a cognitive architecture},
author = {Agnese Augello and Ignazio Infantino and Giovanni Pilato and Riccardo Rizzo and Filippo Vella},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922883096&doi=10.1016%2fj.bica.2014.11.013&partnerID=40&md5=134f251d7f855ed4aaaa905e4281ffed},
doi = {10.1016/j.bica.2014.11.013},
issn = {2212683X},
year = {2015},
date = {2015-01-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {11},
pages = {29–37},
abstract = {Evaluation is a key factor of creativity: for this reason it should be integrated into a cognitive architecture of a creative artificial agent. The approach illustrated in this paper uses the Psi model, and describes the framework for introducing internal and external evaluations, and how they influence demands and motivation of the artificial agent. Internal evaluation mechanisms drive the creative process, and influence competence of the creative agent. External evaluation acts through certainty, and requires interaction with human users that express both opinions and some subjective quantitative evaluations on the final artwork. The system uses natural language processing techniques in order to infer the satisfaction and the emotional impact of the final product obtained by the creative agent. © 2014 Elsevier B.V. All rights reserved.},
keywords = {Artificial intelligence, Cognitive Architectures, Computational Creativity, Creative Process, Creativity Evaluation, Natural Language Processing},
pubstate = {published},
tppubtype = {article}
}
Evaluation is a key factor of creativity: for this reason it should be integrated into a cognitive architecture of a creative artificial agent. The approach illustrated in this paper uses the Psi model, and describes the framework for introducing internal and external evaluations, and how they influence demands and motivation of the artificial agent. Internal evaluation mechanisms drive the creative process, and influence competence of the creative agent. External evaluation acts through certainty, and requires interaction with human users that express both opinions and some subjective quantitative evaluations on the final artwork. The system uses natural language processing techniques in order to infer the satisfaction and the emotional impact of the final product obtained by the creative agent. © 2014 Elsevier B.V. All rights reserved.
2014
Augello, Agnese; Pilato, Giovanni; Vassallo, Giorgio; Gaglio, Salvatore
Chatbots as Interface to Ontologies Journal Article
In: Advances in Intelligent Systems and Computing, vol. 260, pp. 285–299, 2014, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Chatbots, Conversational Agents, Knowledge Representation, Natural Language Processing, Ontologies
@article{augelloChatbotsInterfaceOntologies2014,
title = {Chatbots as Interface to Ontologies},
author = { Agnese Augello and Giovanni Pilato and Giorgio Vassallo and Salvatore Gaglio},
doi = {10.1007/978-3-319-03992-3_20},
issn = {21945357},
year = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {260},
pages = {285--299},
abstract = {Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. textcopyright Springer International Publishing Switzerland 2014.},
keywords = {Chatbots, Conversational Agents, Knowledge Representation, Natural Language Processing, Ontologies},
pubstate = {published},
tppubtype = {article}
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Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. textcopyright Springer International Publishing Switzerland 2014.
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
A Geometric Algebra Based Distributional Model to Encode Sentences Semantics Proceedings Article
In: Lai, C; Giuliani, A; Semeraro, G (Ed.): DISTRIBUTED SYSTEMS AND APPLICATIONS OF INFORMATION FILTERING AND RETRIEVAL: DART 2012: REVISED AND INVITED PAPERS, pp. 101–114, SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2014, ISBN: 978-3-642-40620-1.
Abstract | Links | BibTeX | Tags: Natural Language Processing, Semantic Computing, Semantic Spaces
@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}
}
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.
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Rizzo, Riccardo; Vella, Filippo
Robotic Creativity Driven by Motivation and Semantic Analysis Proceedings Article
In: Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014, pp. 285–289, IEEE Computer Society, 2014, ISBN: 978-1-4799-4002-8.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Chatbots, Cognitive Architectures, Computational Creativity, Creative Agents, Motivation, Natural Language Processing, PSI, Semantic Computing, Social Robots
@inproceedings{augelloRoboticCreativityDriven2014,
title = {Robotic Creativity Driven by Motivation and Semantic Analysis},
author = { Agnese Augello and Ignazio Infantino and Giovanni Pilato and Riccardo Rizzo and Filippo Vella},
doi = {10.1109/ICSC.2014.58},
isbn = {978-1-4799-4002-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014},
pages = {285--289},
publisher = {IEEE Computer Society},
abstract = {The paper proposes a system architecture for artificial creativity that enables a robot to perform portraits. The proposed cognitive architecture is inspired by the PSI model, and it requires that the motivation of the robot in the execution of its tasks is influenced by urges. Such parameters depend on both internal and external evaluation mechanisms. The system is a premise for the development of an artificial artist able to develop a personality and a behavior that depends on its experience of successes and failures (competence), and the availability of different painting techniques (certainty). The creative execution is driven by the motivation arising from the urges, and the perception of the work being executed or performed. The external evaluation is obtained by analyzing the opinions expressed in natural language from people watching the realized portrait. textcopyright 2014 IEEE.},
keywords = {Artificial intelligence, Chatbots, Cognitive Architectures, Computational Creativity, Creative Agents, Motivation, Natural Language Processing, PSI, Semantic Computing, Social Robots},
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The paper proposes a system architecture for artificial creativity that enables a robot to perform portraits. The proposed cognitive architecture is inspired by the PSI model, and it requires that the motivation of the robot in the execution of its tasks is influenced by urges. Such parameters depend on both internal and external evaluation mechanisms. The system is a premise for the development of an artificial artist able to develop a personality and a behavior that depends on its experience of successes and failures (competence), and the availability of different painting techniques (certainty). The creative execution is driven by the motivation arising from the urges, and the perception of the work being executed or performed. The external evaluation is obtained by analyzing the opinions expressed in natural language from people watching the realized portrait. textcopyright 2014 IEEE.
Augello, Agnese; Pilato, Giovanni; Vassallo, Giorgio; Gaglio, Salvatore
Chatbots as interface to ontologies Journal Article
In: Advances in Intelligent Systems and Computing, vol. 260, pp. 285–299, 2014, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Chatbots, Conversational Agents, Knowledge Representation, Natural Language Processing, Ontologies
@article{augello_chatbots_2014,
title = {Chatbots as interface to ontologies},
author = {Agnese Augello and Giovanni Pilato and Giorgio Vassallo and Salvatore Gaglio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903692272&doi=10.1007%2f978-3-319-03992-3_20&partnerID=40&md5=55d82e9b0b5fdbf8176e8c168c729e14},
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journal = {Advances in Intelligent Systems and Computing},
volume = {260},
pages = {285–299},
abstract = {Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. © Springer International Publishing Switzerland 2014.},
keywords = {Chatbots, Conversational Agents, Knowledge Representation, Natural Language Processing, Ontologies},
pubstate = {published},
tppubtype = {article}
}
Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. © Springer International Publishing Switzerland 2014.
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
A Geometric Algebra Based Distributional Model to Encode Sentences Semantics Proceedings Article
In: Lai, C; Giuliani, A; Semeraro, G (Ed.): DISTRIBUTED SYSTEMS AND APPLICATIONS OF INFORMATION FILTERING AND RETRIEVAL: DART 2012: REVISED AND INVITED PAPERS, pp. 101–114, SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2014, ISBN: 978-3-642-40620-1.
Abstract | Links | BibTeX | Tags: Natural Language Processing, Semantic Computing, Semantic Spaces
@inproceedings{augello_geometric_2014,
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}
}
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.
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Rizzo, Riccardo; Vella, Filippo
Robotic creativity driven by motivation and semantic analysis Proceedings Article
In: Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014, pp. 285–289, IEEE Computer Society, 2014, ISBN: 978-1-4799-4002-8.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Chatbots, Cognitive Architectures, Computational Creativity, Creative Agents, Motivation, Natural Language Processing, PSI, Semantic Computing, Social Robots
@inproceedings{augello_robotic_2014,
title = {Robotic creativity driven by motivation and semantic analysis},
author = {Agnese Augello and Ignazio Infantino and Giovanni Pilato and Riccardo Rizzo and Filippo Vella},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906979655&doi=10.1109%2fICSC.2014.58&partnerID=40&md5=4eb7c99a7dc982f4c06c6fdaa3b0cc07},
doi = {10.1109/ICSC.2014.58},
isbn = {978-1-4799-4002-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014},
pages = {285–289},
publisher = {IEEE Computer Society},
abstract = {The paper proposes a system architecture for artificial creativity that enables a robot to perform portraits. The proposed cognitive architecture is inspired by the PSI model, and it requires that the motivation of the robot in the execution of its tasks is influenced by urges. Such parameters depend on both internal and external evaluation mechanisms. The system is a premise for the development of an artificial artist able to develop a personality and a behavior that depends on its experience of successes and failures (competence), and the availability of different painting techniques (certainty). The creative execution is driven by the motivation arising from the urges, and the perception of the work being executed or performed. The external evaluation is obtained by analyzing the opinions expressed in natural language from people watching the realized portrait. © 2014 IEEE.},
keywords = {Artificial intelligence, Chatbots, Cognitive Architectures, Computational Creativity, Creative Agents, Motivation, Natural Language Processing, PSI, Semantic Computing, Social Robots},
pubstate = {published},
tppubtype = {inproceedings}
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The paper proposes a system architecture for artificial creativity that enables a robot to perform portraits. The proposed cognitive architecture is inspired by the PSI model, and it requires that the motivation of the robot in the execution of its tasks is influenced by urges. Such parameters depend on both internal and external evaluation mechanisms. The system is a premise for the development of an artificial artist able to develop a personality and a behavior that depends on its experience of successes and failures (competence), and the availability of different painting techniques (certainty). The creative execution is driven by the motivation arising from the urges, and the perception of the work being executed or performed. The external evaluation is obtained by analyzing the opinions expressed in natural language from people watching the realized portrait. © 2014 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.},
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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.
Sangiorgi, Pierluca; Augello, Agnese; Pilato, Giovanni
An Unsupervised Data-Driven Cross-Lingual Method for Building High Precision Sentiment Lexicons Proceedings Article
In: Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013, pp. 184–190, 2013, ISBN: 978-0-7695-5119-7.
Abstract | Links | BibTeX | Tags: Natural Language Processing, Sentiment Analysis
@inproceedings{sangiorgiUnsupervisedDatadrivenCrosslingual2013,
title = {An Unsupervised Data-Driven Cross-Lingual Method for Building High Precision Sentiment Lexicons},
author = { Pierluca Sangiorgi and Agnese Augello and Giovanni Pilato},
doi = {10.1109/ICSC.2013.40},
isbn = {978-0-7695-5119-7},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013},
pages = {184--190},
abstract = {In this paper we present a completely unsupervised approach for creating a sentiment lexicon. The approach has been realized by designing a pipeline which implements an unsupervised system that covers different aspects: the automatic extraction of user reviews, the pre-processing of text, the use of a scoring measure which combines: entropy, term frequency, inverse document frequency, and finally a cross lingual intersection. We have validated the approach though the analysis of a previews present in the Google Play market. The results show the effectiveness of the approach given by satisfactory values of precision for the obtained lexicon. textcopyright 2013 IEEE.},
keywords = {Natural Language Processing, Sentiment Analysis},
pubstate = {published},
tppubtype = {inproceedings}
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In this paper we present a completely unsupervised approach for creating a sentiment lexicon. The approach has been realized by designing a pipeline which implements an unsupervised system that covers different aspects: the automatic extraction of user reviews, the pre-processing of text, the use of a scoring measure which combines: entropy, term frequency, inverse document frequency, and finally a cross lingual intersection. We have validated the approach though the analysis of a previews present in the Google Play market. The results show the effectiveness of the approach given by satisfactory values of precision for the obtained lexicon. textcopyright 2013 IEEE.
Sangiorgi, Pierluca; Augello, Agnese; Pilato, Giovanni
An unsupervised data-driven cross-lingual method for building high precision sentiment lexicons Proceedings Article
In: Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013, pp. 184–190, 2013, ISBN: 978-0-7695-5119-7.
Abstract | Links | BibTeX | Tags: Natural Language Processing, Sentiment Analysis
@inproceedings{sangiorgi_unsupervised_2013,
title = {An unsupervised data-driven cross-lingual method for building high precision sentiment lexicons},
author = {Pierluca Sangiorgi and Agnese Augello and Giovanni Pilato},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893928537&doi=10.1109%2fICSC.2013.40&partnerID=40&md5=1effc74e444a6393428eb470076091ce},
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year = {2013},
date = {2013-01-01},
booktitle = {Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013},
pages = {184–190},
abstract = {In this paper we present a completely unsupervised approach for creating a sentiment lexicon. The approach has been realized by designing a pipeline which implements an unsupervised system that covers different aspects: the automatic extraction of user reviews, the pre-processing of text, the use of a scoring measure which combines: entropy, term frequency, inverse document frequency, and finally a cross lingual intersection. We have validated the approach though the analysis of a previews present in the Google Play market. The results show the effectiveness of the approach given by satisfactory values of precision for the obtained lexicon. © 2013 IEEE.},
keywords = {Natural Language Processing, Sentiment Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present a completely unsupervised approach for creating a sentiment lexicon. The approach has been realized by designing a pipeline which implements an unsupervised system that covers different aspects: the automatic extraction of user reviews, the pre-processing of text, the use of a scoring measure which combines: entropy, term frequency, inverse document frequency, and finally a cross lingual intersection. We have validated the approach though the analysis of a previews present in the Google Play market. The results show the effectiveness of the approach given by satisfactory values of precision for the obtained lexicon. © 2013 IEEE.
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{augello_clifford_2013,
title = {Clifford rotors for conceptual representation in chatbots},
author = {Agnese Augello and Salvatore Gaglio and Giovanni Pilato and Giorgio Vassallo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870820560&doi=10.1007%2f978-3-642-34274-5_64&partnerID=40&md5=88bd51a58bbdf8bd40b91c9aa9fe16ce},
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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. © 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}
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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. © 2013 Springer-Verlag.
2012
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
Geometric Encoding of Sentences Based on Clifford Algebra Proceedings Article
In: KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 457–462, 2012, ISBN: 978-989-8565-29-7.
Abstract | BibTeX | Tags: Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@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}
}
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.
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
Geometric encoding of sentences based on clifford algebra Proceedings Article
In: KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 457–462, 2012, ISBN: 978-989-8565-29-7.
Abstract | Links | BibTeX | Tags: Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@inproceedings{augello_geometric_2012,
title = {Geometric encoding of sentences based on clifford algebra},
author = {Agnese Augello and Manuel Gentile and Giovanni Pilato and Giorgio Vassallo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881535463&partnerID=40&md5=b6d0cd52d69a3dfc0a811c6390bf0d6c},
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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 © 2012 SciTePress - Science and Technology Publications.},
keywords = {Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
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 © 2012 SciTePress - Science and Technology Publications.
2009
Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore; Pilato, Giovanni
A Semantic Layer on Semi-Structured Data Sources for Intuitive Chatbots Proceedings Article
In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009, pp. 760–765, 2009, ISBN: 978-0-7695-3575-3.
Abstract | Links | BibTeX | Tags: Chatbots, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@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}
}
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.