Thiago Pardo


2022

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Rhetorical Structure Approach for Online Deception Detection: A Survey
Francielle Vargas | Jonas D‘Alessandro | Zohar Rabinovich | Fabrício Benevenuto | Thiago Pardo
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Most information is passed on in the form of language. Therefore, research on how people use language to inform and misinform, and how this knowledge may be automatically extracted from large amounts of text is surely relevant. This survey provides first-hand experiences and a comprehensive review of rhetorical-level structure analysis for online deception detection. We systematically analyze how discourse structure, aligned or not with other approaches, is applied to automatic fake news and fake reviews detection on the web and social media. Moreover, we categorize discourse-tagged corpora along with results, hence offering a summary and accessible introductions to new researchers.

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PortiLexicon-UD: a Portuguese Lexical Resource according to Universal Dependencies Model
Lucelene Lopes | Magali Duran | Paulo Fernandes | Thiago Pardo
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents PortiLexicon-UD, a large and freely available lexicon for Portuguese delivering morphosyntactic information according to the Universal Dependencies model. This lexical resource includes part of speech tags, lemmas, and morphological information for words, with 1,221,218 entries (considering word duplication due to different combination of PoS tag, lemma, and morphological features). We report the lexicon creation process, its computational data structure, and its evaluation over an annotated corpus, showing that it has a high language coverage and good quality data.

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HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection
Francielle Vargas | Isabelle Carvalho | Fabiana Rodrigues de Góes | Thiago Pardo | Fabrício Benevenuto
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Due to the severity of the social media offensive and hateful comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection. The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. The corpus consists of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level classification (highly, moderately, and slightly offensive), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). We also implemented baseline experiments for offensive language and hate speech detection and compared them with a literature baseline. Results show that the baseline experiments on our corpus outperform the current state-of-the-art for the Portuguese language.

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Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese
Rogério Sousa | Thiago Pardo
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of content features and classification methods for two different domains. In particular, we run our experiments for a low resource language – Portuguese –, trying to establish a benchmark for this language. We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution.

2021

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Semantic-Based Opinion Summarization
Marcio Inácio | Thiago Pardo
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

The amount of information available online can be overwhelming for users to digest, specially when dealing with other users’ comments when making a decision about buying a product or service. In this context, opinion summarization systems are of great value, extracting important information from the texts and presenting them to the user in a more understandable manner. It is also known that the usage of semantic representations can benefit the quality of the generated summaries. This paper aims at developing opinion summarization methods based on Abstract Meaning Representation of texts in the Brazilian Portuguese language. Four different methods have been investigated, alongside some literature approaches. The results show that a Machine Learning-based method produced summaries of higher quality, outperforming other literature techniques on manually constructed semantic graphs. We also show that using parsed graphs over manually annotated ones harmed the output. Finally, an analysis of how important different types of information are for the summarization process suggests that using Sentiment Analysis features did not improve summary quality.

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Contextual-Lexicon Approach for Abusive Language Detection
Francielle Vargas | Fabiana Rodrigues de Góes | Isabelle Carvalho | Fabrício Benevenuto | Thiago Pardo
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media, which embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.

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Toward Discourse-Aware Models for Multilingual Fake News Detection
Francielle Vargas | Fabrício Benevenuto | Thiago Pardo
Proceedings of the Student Research Workshop Associated with RANLP 2021

Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief that liars give off cues that may indicate their deception is near-universal. Therefore, given that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people’s cognitive mechanisms have promising guidance for deception detection, in this Ph.D. ongoing research, we propose to examine discourse structure patterns in multilingual deceptive news corpora using the Rhetorical Structure Theory framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual deceptive annotated corpora. Accordingly, this paper describes the current progress in this thesis, including (i) the construction of the first multilingual deceptive corpus, which was annotated by specialists according to the Rhetorical Structure Theory framework, and (ii) the introduction of two new proposed rhetorical relations: INTERJECTION and IMPERATIVE, which we assume to be relevant for the fake news detection task.

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On auxiliary verb in Universal Dependencies: untangling the issue and proposing a systematized annotation strategy
Magali Duran | Adriana Pagano | Amanda Rassi | Thiago Pardo
Proceedings of the Sixth International Conference on Dependency Linguistics (Depling, SyntaxFest 2021)

2020

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Measuring the Impact of Readability Features in Fake News Detection
Roney Santos | Gabriela Pedro | Sidney Leal | Oto Vale | Thiago Pardo | Kalina Bontcheva | Carolina Scarton
Proceedings of the Twelfth Language Resources and Evaluation Conference

The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.

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Semantically Inspired AMR Alignment for the Portuguese Language
Rafael Anchiêta | Thiago Pardo
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Abstract Meaning Representation (AMR) is a graph-based semantic formalism where the nodes are concepts and edges are relations among them. Most of AMR parsing methods require alignment between the nodes of the graph and the words of the sentence. However, this alignment is not provided by manual annotations and available automatic aligners focus only on the English language, not performing well for other languages. Aiming to fulfill this gap, we developed an alignment method for the Portuguese language based on a more semantically matched word-concept pair. We performed both intrinsic and extrinsic evaluations and showed that our alignment approach outperforms the alignment strategies developed for English, improving AMR parsers, and achieving competitive results with a parser designed for the Portuguese language.

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NILC at SR’20: Exploring Pre-Trained Models in Surface Realisation
Marco Antonio Sobrevilla Cabezudo | Thiago Pardo
Proceedings of the Third Workshop on Multilingual Surface Realisation

This paper describes the submission by the NILC Computational Linguistics research group of the University of S ̃ao Paulo/Brazil to the English Track 2 (closed sub-track) at the Surface Realisation Shared Task 2020. The success of the current pre-trained models like BERT or GPT-2 in several tasks is well-known, however, this is not the case for data-to-text generation tasks and just recently some initiatives focused on it. This way, we explore how a pre-trained model (GPT-2) performs on the UD-to-text generation task. In general, the achieved results were poor, but there are some interesting ideas to explore. Among the learned lessons we may note that it is necessary to study strategies to represent UD inputs and to introduce structural knowledge into these pre-trained models.

2019

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Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations
Marco Antonio Sobrevilla Cabezudo | Simon Mille | Thiago Pardo
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.

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Towards a General Abstract Meaning Representation Corpus for Brazilian Portuguese
Marco Antonio Sobrevilla Cabezudo | Thiago Pardo
Proceedings of the 13th Linguistic Annotation Workshop

Abstract Meaning Representation (AMR) is a recent and prominent semantic representation with good acceptance and several applications in the Natural Language Processing area. For English, there is a large annotated corpus (with approximately 39K sentences) that supports the research with the representation. However, to the best of our knowledge, there is only one restricted corpus for Portuguese, which contains 1,527 sentences. In this context, this paper presents an effort to build a general purpose AMR-annotated corpus for Brazilian Portuguese by translating and adapting AMR English guidelines. Our results show that such approach is feasible, but there are some challenging phenomena to solve. More than this, efforts are necessary to increase the coverage of the corresponding lexical resource that supports the annotation.

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Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language
Marco Antonio Sobrevilla Cabezudo | Thiago Pardo
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

This paper presents a more recent literature review on Natural Language Generation. In particular, we highlight the efforts for Brazilian Portuguese in order to show the available resources and the existent approaches for this language. We also focus on the approaches for generation from semantic representations (emphasizing the Abstract Meaning Representation formalism) as well as their advantages and limitations, including possible future directions.

2018

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NILC-SWORNEMO at the Surface Realization Shared Task: Exploring Syntax-Based Word Ordering using Neural Models
Marco Antonio Sobrevilla Cabezudo | Thiago Pardo
Proceedings of the First Workshop on Multilingual Surface Realisation

This paper describes the submission by the NILC Computational Linguistics research group of the University of São Paulo/Brazil to the Track 1 of the Surface Realization Shared Task (SRST Track 1). We present a neural-based method that works at the syntactic level to order the words (which we refer by NILC-SWORNEMO, standing for “Syntax-based Word ORdering using NEural MOdels”). Additionally, we apply a bottom-up approach to build the sentence and, using language-specific lexicons, we produce the proper word form of each lemma in the sentence. The results obtained by our method outperformed the average of the results for English, Portuguese and Spanish in the track.

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Towards AMR-BR: A SemBank for Brazilian Portuguese Language
Rafael Anchiêta | Thiago Pardo
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Improving Opinion Summarization by Assessing Sentence Importance in On-line Reviews
Rafael Anchiêta | Rogerio Figueredo Sousa | Raimundo Moura | Thiago Pardo
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology

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Formação de gentílicos a partir de topônimos: descrição linguística e aprendizado automático (Formation of Demonyms from Toponyms: Linguistic Description and Machine Learning)[In Portuguese]
Roger Alfredo Marci Rodrigues Antunes | Thiago Pardo | Gladis Maria Barcelos Almeida
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology

2015

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A Qualitative Analysis of a Corpus of Opinion Summaries based on Aspects
Roque López | Thiago Pardo | Lucas Avanço | Pedro Filho | Alessandro Bokan | Paula Cardoso | Márcio Dias | Fernando Nóbrega | Marco Cabezudo | Jackson Souza | Andressa Zacarias | Eloize Seno | Ariani Di Felippo
Proceedings of the 9th Linguistic Annotation Workshop

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A Discursive Grid Approach to Model Local Coherence in Multi-document Summaries
Márcio Dias | Thiago Pardo
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

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NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages
Pedro Balage Filho | Lucas Avanço | Thiago Pardo | Maria das Graças Volpe Nunes
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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NILC_USP: Aspect Extraction using Semantic Labels
Pedro Balage Filho | Thiago Pardo
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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A Large Corpus of Product Reviews in Portuguese: Tackling Out-Of-Vocabulary Words
Nathan Hartmann | Lucas Avanço | Pedro Balage | Magali Duran | Maria das Graças Volpe Nunes | Thiago Pardo | Sandra Aluísio
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Web 2.0 has allowed a never imagined communication boom. With the widespread use of computational and mobile devices, anyone, in practically any language, may post comments in the web. As such, formal language is not necessarily used. In fact, in these communicative situations, language is marked by the absence of more complex syntactic structures and the presence of internet slang, with missing diacritics, repetitions of vowels, and the use of chat-speak style abbreviations, emoticons and colloquial expressions. Such language use poses severe new challenges for Natural Language Processing (NLP) tools and applications, which, so far, have focused on well-written texts. In this work, we report the construction of a large web corpus of product reviews in Brazilian Portuguese and the analysis of its lexical phenomena, which support the development of a lexical normalization tool for, in future work, subsidizing the use of standard NLP products for web opinion mining and summarization purposes.

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Some Issues on the Normalization of a Corpus of Products Reviews in Portuguese
Magali Sanches Duran | Lucas Avanço | Sandra Aluísio | Thiago Pardo | Maria da Graça Volpe Nunes
Proceedings of the 9th Web as Corpus Workshop (WaC-9)

2013

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A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set
Merley Conrado | Thiago Pardo | Solange Rezende
Proceedings of the 2013 NAACL HLT Student Research Workshop

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On the contribution of discourse structure to topic segmentation
Paula Cardoso | Maite Taboada | Thiago Pardo
Proceedings of the SIGDIAL 2013 Conference

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NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages
Pedro Balage Filho | Thiago Pardo
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2011

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Experiments on Term Extraction using Noun Phrase Subclassifications
Merley da Silva Conrado | Walter Koza | Josuka Díaz-Labrador | Joseba Abaitua | Solange Oliveira Rezende | Thiago Pardo | Zulema Solana
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Computational Linguistics in Brazil: An Overview
Thiago Pardo | Caroline Gasperin | Helena de Medeiros Caseli | Maria das Graças Nunes
Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas

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Experiments with CST-Based Multidocument Summarization
Maria Lucía Castro Jorge | Thiago Pardo
Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing

2009

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Supporting the Adaptation of Texts for Poor Literacy Readers: a Text Simplification Editor for Brazilian Portuguese
Arnaldo Candido | Erick Maziero | Lucia Specia | Caroline Gasperin | Thiago Pardo | Sandra Aluisio
Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications