Publications

@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}

}

@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}

}