3 Search Results for "Nunes, Luis"


Document
Question Answering For Toxicological Information Extraction

Authors: Bruno Carlos Luís Ferreira, Hugo Gonçalo Oliveira, Hugo Amaro, Ângela Laranjeiro, and Catarina Silva

Published in: OASIcs, Volume 104, 11th Symposium on Languages, Applications and Technologies (SLATE 2022)


Abstract
Working with large amounts of text data has become hectic and time-consuming. In order to reduce human effort, costs, and make the process more efficient, companies and organizations resort to intelligent algorithms to automate and assist the manual work. This problem is also present in the field of toxicological analysis of chemical substances, where information needs to be searched from multiple documents. That said, we propose an approach that relies on Question Answering for acquiring information from unstructured data, in our case, English PDF documents containing information about physicochemical and toxicological properties of chemical substances. Experimental results confirm that our approach achieves promising results which can be applicable in the business scenario, especially if further revised by humans.

Cite as

Bruno Carlos Luís Ferreira, Hugo Gonçalo Oliveira, Hugo Amaro, Ângela Laranjeiro, and Catarina Silva. Question Answering For Toxicological Information Extraction. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 3:1-3:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@InProceedings{ferreira_et_al:OASIcs.SLATE.2022.3,
  author =	{Ferreira, Bruno Carlos Lu{\'\i}s and Gon\c{c}alo Oliveira, Hugo and Amaro, Hugo and Laranjeiro, \^{A}ngela and Silva, Catarina},
  title =	{{Question Answering For Toxicological Information Extraction}},
  booktitle =	{11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
  pages =	{3:1--3:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-245-7},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{104},
  editor =	{Cordeiro, Jo\~{a}o and Pereira, Maria Jo\~{a}o and Rodrigues, Nuno F. and Pais, Sebasti\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2022.3},
  URN =		{urn:nbn:de:0030-drops-167493},
  doi =		{10.4230/OASIcs.SLATE.2022.3},
  annote =	{Keywords: Information Extraction, Question Answering, Transformers, Toxicological Analysis}
}
Document
Classification of Public Administration Complaints

Authors: Francisco Caldeira, Luís Nunes, and Ricardo Ribeiro

Published in: OASIcs, Volume 104, 11th Symposium on Languages, Applications and Technologies (SLATE 2022)


Abstract
Complaint management is a problem faced by many organizations that is both vital to customer image and highly dependent on human resources. This work attempts to tackle a part of the problem, by classifying summaries of complaints using machine learning models in order to better redirect these to the appropriate responders. The main challenges of this task is that training datasets are often small and highly imbalanced. This can can have a big impact on the performance of classification models. The dataset analyzed in this work suffers from both of these problems, being relatively small and having labels in different proportions. In this work, two different techniques are analyzed: combining classes together to increase the number of elements of the new class; and, providing new artificial examples for some classes via translation into other languages. The classification models explored were the following: k-NN, SVM, Naïve Bayes, boosting, and Deep Learning approaches, including transformers. The paper concludes that although, as expected, the classes with little representation are hard to classify, the techniques explored helped to boost the performance, especially in the classes with a low number of elements. SVM and BERT-based models outperformed their peers.

Cite as

Francisco Caldeira, Luís Nunes, and Ricardo Ribeiro. Classification of Public Administration Complaints. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 9:1-9:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@InProceedings{caldeira_et_al:OASIcs.SLATE.2022.9,
  author =	{Caldeira, Francisco and Nunes, Lu{\'\i}s and Ribeiro, Ricardo},
  title =	{{Classification of Public Administration Complaints}},
  booktitle =	{11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
  pages =	{9:1--9:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-245-7},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{104},
  editor =	{Cordeiro, Jo\~{a}o and Pereira, Maria Jo\~{a}o and Rodrigues, Nuno F. and Pais, Sebasti\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2022.9},
  URN =		{urn:nbn:de:0030-drops-167555},
  doi =		{10.4230/OASIcs.SLATE.2022.9},
  annote =	{Keywords: Text Classification, Natural Language Processing, Deep Learning, BERT}
}
Document
Comparing Different Methods for Disfluency Structure Detection

Authors: Henrique Medeiros, Fernando Batista, Helena Moniz, Isabel Trancoso, and Luis Nunes

Published in: OASIcs, Volume 29, 2nd Symposium on Languages, Applications and Technologies (2013)


Abstract
This paper presents a number of experiments focusing on assessing the performance of different machine learning methods on the identification of disfluencies and their distinct structural regions over speech data. Several machine learning methods have been applied, namely Naive Bayes, Logistic Regression, Classification and Regression Trees (CARTs), J48 and Multilayer Perceptron. Our experiments show that CARTs outperform the other methods on the identification of the distinct structural disfluent regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32h of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. This work shows that using fully automatic prosodic features, disfluency structural regions can be reliably identified using CARTs, where the best results achieved correspond to 81.5% precision, 27.6% recall, and 41.2% F-measure. The best results concern the detection of the interregnum, followed by the detection of the interruption point.

Cite as

Henrique Medeiros, Fernando Batista, Helena Moniz, Isabel Trancoso, and Luis Nunes. Comparing Different Methods for Disfluency Structure Detection. In 2nd Symposium on Languages, Applications and Technologies. Open Access Series in Informatics (OASIcs), Volume 29, pp. 259-269, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)


Copy BibTex To Clipboard

@InProceedings{medeiros_et_al:OASIcs.SLATE.2013.259,
  author =	{Medeiros, Henrique and Batista, Fernando and Moniz, Helena and Trancoso, Isabel and Nunes, Luis},
  title =	{{Comparing Different Methods for Disfluency Structure Detection}},
  booktitle =	{2nd Symposium on Languages, Applications and Technologies},
  pages =	{259--269},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-52-1},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{29},
  editor =	{Leal, Jos\'{e} Paulo and Rocha, Ricardo and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2013.259},
  URN =		{urn:nbn:de:0030-drops-40420},
  doi =		{10.4230/OASIcs.SLATE.2013.259},
  annote =	{Keywords: Machine learning, speech processing, prosodic features, automatic detection of disfluencies}
}
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