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 use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2016
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
A Word Prediction Methodology Based on Posgrams Journal Article
In: Communications in Computer and Information Science, vol. 631, pp. 139–154, 2016, ISSN: 18650929.
Abstract | Links | BibTeX | Tags: Knowledge Management, Semantic Computing
@article{spicciaWordPredictionMethodology2016,
title = {A Word Prediction Methodology Based on Posgrams},
author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = { Fred A. Dietz J.L.G. Liu K. Aveiro D. Filipe J.},
doi = {10.1007/978-3-319-52758-1_9},
issn = {18650929},
year = {2016},
date = {2016-01-01},
journal = {Communications in Computer and Information Science},
volume = {631},
pages = {139--154},
abstract = {This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on ``posgrams'' analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. textcopyright Springer International Publishing AG 2016.},
keywords = {Knowledge Management, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on ``posgrams'' analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. textcopyright Springer International Publishing AG 2016.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
A word prediction methodology based on posgrams Journal Article
In: Communications in Computer and Information Science, vol. 631, pp. 139–154, 2016, ISSN: 18650929.
Abstract | Links | BibTeX | Tags: Knowledge Management, Semantic Computing
@article{spiccia_word_2016,
title = {A word prediction methodology based on posgrams},
author = {Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = {Dietz J. L. G. Fred A. Filipe J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011357024&doi=10.1007%2f978-3-319-52758-1_9&partnerID=40&md5=ab1115d3da5af0239340ab31566e7401},
doi = {10.1007/978-3-319-52758-1_9},
issn = {18650929},
year = {2016},
date = {2016-01-01},
journal = {Communications in Computer and Information Science},
volume = {631},
pages = {139–154},
abstract = {This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on “posgrams” analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. © Springer International Publishing AG 2016.},
keywords = {Knowledge Management, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on “posgrams” analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition. © Springer International Publishing AG 2016.
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
Posgram Driven Word Prediction Proceedings Article
In: A., Dietz J. Aveiro D. Liu K. Filipe J. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 589–596, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Semantic Computing
@inproceedings{spicciaPosgramDrivenWord2015,
title = {Posgram Driven Word Prediction},
author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = { Dietz J. Aveiro D. Liu K. Filipe J. Filipe J. Fred A.},
doi = {10.5220/0005613305890596},
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 = {589--596},
publisher = {SciTePress},
abstract = {Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
Posgram driven word prediction Proceedings Article
In: A., Aveiro D. Dietz J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 589–596, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Semantic Computing
@inproceedings{spiccia_posgram_2015,
title = {Posgram driven word prediction},
author = {Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = {Aveiro D. Dietz J. Fred A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960852021&doi=10.5220%2f0005613305890596&partnerID=40&md5=8ac11d2bbf2bb8953abb4c966c37eea1},
doi = {10.5220/0005613305890596},
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 = {589–596},
publisher = {SciTePress},
abstract = {Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Ditta, Marilena; Milazzo, Fabrizio; Raví, Valentina; Pilato, Giovanni; Augello, Agnese
Data-driven relation discovery from unstructured texts Proceedings Article
In: A., Liu K. 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 | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing
@inproceedings{ditta_data-driven_2015,
title = {Data-driven relation discovery from unstructured texts},
author = {Marilena Ditta and Fabrizio Milazzo and Valentina Raví and Giovanni Pilato and Agnese Augello},
editor = {Liu K. Filipe J. Fred A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960877482&partnerID=40&md5=3e9c3192a44eab571fd16c461fc4008d},
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. © 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. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.