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Documents authored by Rodrigues, Mário

Document
Complete Volume
DOI: 10.4230/OASIcs.SLATE.2024
OASIcs, Volume 120, SLATE 2024, Complete Volume

Authors: Mário Rodrigues, José Paulo Leal, and Filipe Portela

Published in: OASIcs, Volume 120, 13th Symposium on Languages, Applications and Technologies (SLATE 2024)

Abstract
OASIcs, Volume 120, SLATE 2024, Complete Volume
Cite as

13th Symposium on Languages, Applications and Technologies (SLATE 2024). Open Access Series in Informatics (OASIcs), Volume 120, pp. 1-186, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Proceedings{rodrigues_et_al:OASIcs.SLATE.2024,
  title =	{{OASIcs, Volume 120, SLATE 2024, Complete Volume}},
  booktitle =	{13th Symposium on Languages, Applications and Technologies (SLATE 2024)},
  pages =	{1--186},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-321-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{120},
  editor =	{Rodrigues, M\'{a}rio and Leal, Jos\'{e} Paulo and Portela, Filipe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2024},
  URN =		{urn:nbn:de:0030-drops-220911},
  doi =		{10.4230/OASIcs.SLATE.2024},
  annote =	{Keywords: OASIcs, Volume 120, SLATE 2024, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.SLATE.2024.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Mário Rodrigues, José Paulo Leal, and Filipe Portela

Published in: OASIcs, Volume 120, 13th Symposium on Languages, Applications and Technologies (SLATE 2024)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

13th Symposium on Languages, Applications and Technologies (SLATE 2024). Open Access Series in Informatics (OASIcs), Volume 120, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{rodrigues_et_al:OASIcs.SLATE.2024.0,
  author =	{Rodrigues, M\'{a}rio and Leal, Jos\'{e} Paulo and Portela, Filipe},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{13th Symposium on Languages, Applications and Technologies (SLATE 2024)},
  pages =	{0:i--0:xii},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-321-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{120},
  editor =	{Rodrigues, M\'{a}rio and Leal, Jos\'{e} Paulo and Portela, Filipe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2024.0},
  URN =		{urn:nbn:de:0030-drops-220906},
  doi =		{10.4230/OASIcs.SLATE.2024.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.SLATE.2024.1
Using Embeddings to Improve Named Entity Recognition Classification with Graphs

Authors: Gabriel Silva, Mário Rodrigues, António Teixeira, and Marlene Amorim

Published in: OASIcs, Volume 120, 13th Symposium on Languages, Applications and Technologies (SLATE 2024)

Abstract
Richer information has potential to improve performance of NLP (Natural Language Processing) tasks such as Named Entity Recognition. A linear sequence of words can be enriched with the sentence structure, as well as their syntactic structure. However, traditional NLP methods do not contemplate this kind of information. With the use of Knowledge Graphs all this information can be represented and made use off by Graph ML (Machine Learning) techniques. Previous experiments using only graphs with their syntactic structure as input to current state-of-the-art Graph ML models failed to prove the potential of the technology. As such, in this paper the use of word embeddings is explored as an additional enrichment of the graph and, in consequence, of the input to the classification models. This use of embeddings adds a layer of context that was previously missing when using only syntactic information. The proposed method was assessed using CoNLL dataset and results showed noticeable improvements in performance when adding embeddings. The best accuracy results with embedings attained 94.73 % accuracy, compared to the 88.58 % without embedings while metrics such as Macro-F1, Precision and Recall achieved an improvement in performance of over 20%. We test these models with a different number of classes to assess whether the quality of them would degrade or not. Due to the use of inductive learning methods (such as Graph SAGE) these results provide us with models that can be used in real-world scenarios as there is no need to re-train the whole graph to predict on new data points as is the case with traditional Graph ML methods (for example, Graph Convolutional Networks).
Cite as

Gabriel Silva, Mário Rodrigues, António Teixeira, and Marlene Amorim. Using Embeddings to Improve Named Entity Recognition Classification with Graphs. In 13th Symposium on Languages, Applications and Technologies (SLATE 2024). Open Access Series in Informatics (OASIcs), Volume 120, pp. 1:1-1:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{silva_et_al:OASIcs.SLATE.2024.1,
  author =	{Silva, Gabriel and Rodrigues, M\'{a}rio and Teixeira, Ant\'{o}nio and Amorim, Marlene},
  title =	{{Using Embeddings to Improve Named Entity Recognition Classification with Graphs}},
  booktitle =	{13th Symposium on Languages, Applications and Technologies (SLATE 2024)},
  pages =	{1:1--1:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-321-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{120},
  editor =	{Rodrigues, M\'{a}rio and Leal, Jos\'{e} Paulo and Portela, Filipe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2024.1},
  URN =		{urn:nbn:de:0030-drops-220722},
  doi =		{10.4230/OASIcs.SLATE.2024.1},
  annote =	{Keywords: Knowledge graphs, Enriched data, Natural language processing, Named Entity Recognition}
}
Document
DOI: 10.4230/OASIcs.SLATE.2023.2
A Framework for Fostering Easier Access to Enriched Textual Information

Authors: Gabriel Silva, Mário Rodrigues, António Teixeira, and Marlene Amorim

Published in: OASIcs, Volume 113, 12th Symposium on Languages, Applications and Technologies (SLATE 2023)

Abstract
Considering the amount of information in unstructured data it is necessary to have suitable methods to extract information from it. Most of these methods have their own output making it difficult and costly to merge and share this information as there currently is no unified way of representing this information. While most of these methods rely on JSON or XML there has been a push to serialize these into RDF compliant formats due to their flexiblity and the existing ecosystem surrounding them. In this paper we introduce a framework whose goal is to provide a serialization of enriched data into an RDF format, following FAIR principles, making it more interpretable, interoperable and shareable. We process a subset of the WikiNER dataset and showcase two examples of using this framework: One using CoNLL annotations and the other by performing entity-linking on an already existing graph. The results are a graph with every connection starting from the document and finishing on tokens while keeping the original text intact while embedding the enriched data into it, in this case the CoNLL annotations and Entities.
Cite as

Gabriel Silva, Mário Rodrigues, António Teixeira, and Marlene Amorim. A Framework for Fostering Easier Access to Enriched Textual Information. In 12th Symposium on Languages, Applications and Technologies (SLATE 2023). Open Access Series in Informatics (OASIcs), Volume 113, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{silva_et_al:OASIcs.SLATE.2023.2,
  author =	{Silva, Gabriel and Rodrigues, M\'{a}rio and Teixeira, Ant\'{o}nio and Amorim, Marlene},
  title =	{{A Framework for Fostering Easier Access to Enriched Textual Information}},
  booktitle =	{12th Symposium on Languages, Applications and Technologies (SLATE 2023)},
  pages =	{2:1--2:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-291-4},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{113},
  editor =	{Sim\~{o}es, Alberto and Ber\'{o}n, Mario Marcelo and Portela, Filipe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2023.2},
  URN =		{urn:nbn:de:0030-drops-185165},
  doi =		{10.4230/OASIcs.SLATE.2023.2},
  annote =	{Keywords: Knowledge graphs, Enriched data, Natural language processing, Triplestore}
}
Document
DOI: 10.4230/OASIcs.SLATE.2021.11
Towards Automatic Creation of Annotations to Foster Development of Named Entity Recognizers

Authors: Emanuel Matos, Mário Rodrigues, Pedro Miguel, and António Teixeira

Published in: OASIcs, Volume 94, 10th Symposium on Languages, Applications and Technologies (SLATE 2021)

Abstract
Named Entity Recognition (NER) is an essential step for many natural language processing tasks, including Information Extraction. Despite recent advances, particularly using deep learning techniques, the creation of accurate named entity recognizers continues a complex task, highly dependent on annotated data availability. To foster existence of NER systems for new domains it is crucial to obtain the required large volumes of annotated data with low or no manual labor. In this paper it is proposed a system to create the annotated data automatically, by resorting to a set of existing NERs and information sources (DBpedia). The approach was tested with documents of the Tourism domain. Distinct methods were applied for deciding the final named entities and respective tags. The results show that this approach can increase the confidence on annotations and/or augment the number of categories possible to annotate. This paper also presents examples of new NERs that can be rapidly created with the obtained annotated data. The annotated data, combined with the possibility to apply both the ensemble of NER systems and the new Gazetteer-based NERs to large corpora, create the necessary conditions to explore the recent neural deep learning state-of-art approaches to NER (ex: BERT) in domains with scarce or nonexistent data for training.
Cite as

Emanuel Matos, Mário Rodrigues, Pedro Miguel, and António Teixeira. Towards Automatic Creation of Annotations to Foster Development of Named Entity Recognizers. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 11:1-11:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{matos_et_al:OASIcs.SLATE.2021.11,
  author =	{Matos, Emanuel and Rodrigues, M\'{a}rio and Miguel, Pedro and Teixeira, Ant\'{o}nio},
  title =	{{Towards Automatic Creation of Annotations to Foster Development of Named Entity Recognizers}},
  booktitle =	{10th Symposium on Languages, Applications and Technologies (SLATE 2021)},
  pages =	{11:1--11:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-202-0},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{94},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Portela, Filipe and Pereira, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2021.11},
  URN =		{urn:nbn:de:0030-drops-144286},
  doi =		{10.4230/OASIcs.SLATE.2021.11},
  annote =	{Keywords: Named Entity Recognition (NER), Automatic Annotation, Gazetteers, Tourism, Portuguese}
}
Document
DOI: 10.4230/OASIcs.SLATE.2017.20
Natural Transmission of Information Extraction Results to End-Users - A Proof-of-Concept Using Data-to-Text

Authors: José Casimiro Pereira, António J. S. Teixeira, Mário Rodrigues, Pedro Miguel, and Joaquim Sousa Pinto

Published in: OASIcs, Volume 56, 6th Symposium on Languages, Applications and Technologies (SLATE 2017)

Abstract
Information Extraction from natural texts has a great potential in areas such as Tourism and can be of great assistance in transforming customers' comments in valuable information for Tourism operators, governments and customers. After extraction, information needs to be efficiently transmitted to end-users in a natural way. Systems should not, in general, send extracted information directly to end-users, such as hotel managers, as it can be difficult to read. Naturally, humans transmit and encode information using natural languages, such as Portuguese. The problem arising from the need of efficient and natural transmission of the information to end-user is how to encode it. The use of natural language generation (NLG) is a possible solution, for producing sentences, and, with them, texts. In this paper we address this, with a data-to-text system, a derivation of formal NLG systems that use data as input. The proposed system uses an aligned corpus, which was defined, collected and processed, in about approximately 3 weeks of work. To build the language model were used three different in-domain and out-of-domain corpora. The effects of this approach were evaluated, and results are presented. Automatic metrics, BLEU and Meteor, were used to evaluate the different systems, comparing their values with similar systems. Results show that expanding the corpus has a major positive effect in BLEU and Meteor scores and use of additional corpora (in-domain and out-of-domain) in training language model does not result in significantly different performance. The scores obtained, combined with their comparison with other systems performance and informal evaluation by humans of the sentences produced, give additional support for the capabilities of the translation based approach for fast development of data-to-text for new domains.
Cite as

José Casimiro Pereira, António J. S. Teixeira, Mário Rodrigues, Pedro Miguel, and Joaquim Sousa Pinto. Natural Transmission of Information Extraction Results to End-Users - A Proof-of-Concept Using Data-to-Text. In 6th Symposium on Languages, Applications and Technologies (SLATE 2017). Open Access Series in Informatics (OASIcs), Volume 56, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{pereira_et_al:OASIcs.SLATE.2017.20,
  author =	{Pereira, Jos\'{e} Casimiro and Teixeira, Ant\'{o}nio J. S. and Rodrigues, M\'{a}rio and Miguel, Pedro and Pinto, Joaquim Sousa},
  title =	{{Natural Transmission of Information Extraction Results to End-Users - A Proof-of-Concept Using Data-to-Text}},
  booktitle =	{6th Symposium on Languages, Applications and Technologies (SLATE 2017)},
  pages =	{20:1--20:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-056-9},
  ISSN =	{2190-6807},
  year =	{2017},
  volume =	{56},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Leal, Jos\'{e} Paulo and Varanda, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2017.20},
  URN =		{urn:nbn:de:0030-drops-79530},
  doi =		{10.4230/OASIcs.SLATE.2017.20},
  annote =	{Keywords: Data-to-Text, Natural Language Generation, Automatic Translation, opinions, Tourism, Portuguese}
}
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