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Documents authored by Reis, Sónia

Document
DOI: 10.4230/OASIcs.SLATE.2025.9
Semantic Representation of Adverbs in the Lexicalized Meaning Representation (LMR) Framework

Authors: Jorge Baptista, Izabela Müller, and Sónia Reis

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract
Semantic parsing serves as a crucial interface between natural language and formal meaning representations, enabling computational systems to capture the underlying semantic structure of linguistic expressions. This paper addresses a relatively understudied area in both linguistic theory and natural language processing: the semantic representation of adverbs. We conduct a comparative analysis of annotation guidelines and practices across two semantic representation frameworks: Lexicalized Meaning Representation (LMR), applied to the European Portuguese edition of the novella "O Principezinho" by Antoine de Saint-Exupéry (1943); and Abstract Meaning Representation (AMR), applied to the Brazilian Portuguese edition, "O Pequeno Príncipe". The study reveals significant limitations in AMR’s handling of adverbial constructions, particularly when assessed against contemporary syntactic-semantic advances in linguistic theory. Furthermore, it highlights the theoretical and practical challenges that LMR continues to face in this domain.
Cite as

Jorge Baptista, Izabela Müller, and Sónia Reis. Semantic Representation of Adverbs in the Lexicalized Meaning Representation (LMR) Framework. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 9:1-9:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{baptista_et_al:OASIcs.SLATE.2025.9,
  author =	{Baptista, Jorge and M\"{u}ller, Izabela and Reis, S\'{o}nia},
  title =	{{Semantic Representation of Adverbs in the Lexicalized Meaning Representation (LMR) Framework}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{9:1--9:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.9},
  URN =		{urn:nbn:de:0030-drops-236891},
  doi =		{10.4230/OASIcs.SLATE.2025.9},
  annote =	{Keywords: Semantic representation, Adverbs, Lexicalized Meaning Representation (LMR), Abstract Meaning Representation (AMR), Annotation guidelines, European Portuguese, Brazilian Portuguese, Comparative analysis, The Little Prince, Corpus linguistics, Natural Language Processing (NLP), Multi-word expressions, Syntactic-semantic interface, Linguistic theory}
}
Document
DOI: 10.4230/OASIcs.SLATE.2022.2
Automatic Classification of Portuguese Proverbs

Authors: Jorge Baptista and Sónia Reis

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

Abstract
In this paper, natural language processing (NLP) and machine learning methods and tools are applied to the task of topic (thematic or semantic) classification of Portuguese proverbs. This is a difficult task since proverbs are usually very short sentences. Such classification should allow an easier selection of the most relevant proverbs for a given situation, considering their context in discourse or within a text. For that, we used, on the one hand, a collection of +32,000 proverbial expressions organized "thematically" into a large set of previously attributed topics (+2,200) and, on the other hand, the Orange data mining toolkit, along with the NLP and machine learning tools it provides. Since the classification provided in the collection of proverbs is, for the most part, based only on a keyword in the body of the proverbs, 2 experiments were set up, to determine the feasibility of the task with a modicum of effort and the most promising configurations applicable. Different sample sizes, 100 and 50 proverbs randomly selected per topic, corresponding to Scenario 1 and 2, respectively, were contrasted; several preprocessing strategies were explored, and different data representation methods tested against several learning algorithms. Results show that Neural Networks is the best performing model, achieving the best classification accuracy of 70% and 61%, in the two different experimental scenarios, Scenario 1 and 2, respectively. Some of the inaccurate classification cases seem to indicate that the machine learning approach can sometimes do a better job than a human classifier, especially considering the manual attribution of the topics by the collection’s author, the sheer number of topics involved, and the very unbalanced distribution of proverbs per topic. Based on the results achieved, the paper presents some proposals for future work to cope with such difficulties.
Cite as

Jorge Baptista and Sónia Reis. Automatic Classification of Portuguese Proverbs. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 2:1-2:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{baptista_et_al:OASIcs.SLATE.2022.2,
  author =	{Baptista, Jorge and Reis, S\'{o}nia},
  title =	{{Automatic Classification of Portuguese Proverbs}},
  booktitle =	{11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
  pages =	{2:1--2:8},
  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.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2022.2},
  URN =		{urn:nbn:de:0030-drops-167480},
  doi =		{10.4230/OASIcs.SLATE.2022.2},
  annote =	{Keywords: Portuguese Proverbs, Automatic Topic Classification, Machine Learning}
}
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