Brazil

We compiled a set of patterns that denote lexico-semantic relations in Portuguese, created templates where one argument is fixed and the other is replaced by a mask, and then used BERT for predicting the latter. For most relations and measures, when assessed in a test of lexico-semantic analogies, BERT predictions outperformed those of earlier methods computed from static word embeddings, either with a pattern alone or with a combination of patterns. There is still a large margin of progression, but this suggests that BERT can be used as a source of lexico-semantic knowledge in Portuguese.

PROPOR 2022

Drilling Lexico-Semantic Knowledge in Portuguese from BERT

semantic relations

masked language model

bert

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician. The meeting has been a very rich forum for the exchange of ideas and partnerships for the research and industry communities dedicated to the automated processing of Portuguese, promoting the development of methodologies, resources and projects that can be shared among researchers and practitioners in the field.

Find more information and pay the registration fee on [event organizer’s website](https://sites.universidadedefortaleza.com/propor2022/?page_id=401)

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician.

Dependency parsing can enhance the performance of Named Entity Recognition (NER) models and can be leveraged to boost information extraction. NER tasks are essential to deal with clinical narratives, but models for Brazilian Portuguese dependency parsing are scarce, even less for clinical texts and their specificities. This paper reports on the development of a treebank of clinical narratives in Brazilian Portuguese and the drafting of guidelines. Based on a corpus of 1,000 clinical narratives manually annotated with semantic information, split into 12,711 sentences, we identified some characteristics of these texts that differ from traditional domains and have a deep impact on the annotation process, such as extensive use of acronyms and abbreviations, words not recognized by POS taggers, misspelling, special use of some symbols, different uses for numerals, heterogeneity of sentence sizes, and coordinated phrases without any punctuation. We developed a document to describe the annotation types and to explain how difficult cases should be treated to ensure consistency, including examples that could be found in this kind of text. We created a Tag versus Frequency relation to justify some of the characteristics and challenges of the corpus. The corpus when completely annotated will be made available to the entire scientific community that performs research with clinical texts.

Challenges in annotating a treebank of clinical narratives in Brazilian Portuguese

This paper aims to summarize the key points and results of an analysis of the alignment of gesture and prosodic boundaries collected through manual annotation and automated treatment. For this purpose, we used a multimodal corpus of spontaneous speech, the BGEST corpus (Barros, 2021), part of the C-ORAL-BRASIL language resources (Moneglia and Cresti, 2005; Raso and Mello, 2012). Gestures and prosodic boundaries were manually annotated with ELAN. The gestural an-notation followed partly the Linguistic Annotation System for Gestures guidelines (Bressem et al., 2013), whereas the prosodic boundaries followed the perceptual criterion adopted in other C-ORAL resources. The tabular data outputted by ELAN was then analyzed by a script that compares the alignment of gesture phrases and intonation units. The results point to a larger gestural phrase that encompasses the intonation unit, while the stroke is almost always within the intonation unit. Around 85 percent of all information units align with a gesture. The analysis also points that the time values of alignment are shorter than analyzed by Loehr (2004) both for initial as well for final boundaries and more complex than proposed by Cantalini (2018). This indicates that more research is needed before we can set an approximate value for the overlap of larger units.

A protocol for comparing gesture and prosodic boundaries in multimodal corpora

Palatal consonants in Portuguese are considered complex or marked segments because they are inherently heavy and restricted in terms of their distribution, in relation to other consonants. Moreover, they appear to display differences between themselves, as first language acquisition and creoles' adaptation suggest that /ʎ/ is more complex than /ɲ/. The arguments for complexity are endorsed by some qualitative studies but are still lacking quantitative support. This paper aims at analyzing the phonological restrictiveness of these consonants by comparing their actual frequency in several different corpora, reporting both lexical entries and usage in discourse. In addition to their context-free frequency, we control for their word position and phonetic adjacency. We find that palatals are less frequent than other consonants. However, relative to each other, they do not display proportional lexical and usage frequencies. These results shed new light not only on the representation of /ɲ/ and /ʎ/but also on the relation between frequency and markedness in language studies.

Comparing lexical and usage frequencies of palatal segments in Portuguese

Forced phonetic alignment (FPA) is the task of assessing the time boundaries of phonetic units, i.e., calculating when in the speech utterance a certain phoneme starts and ends. This paper describes experiments on FPA for Brazilian Portuguese using Kaldi toolkit. Based on time-delay neural networks (TDNN), several acoustic models were trained on the top of the combination between hidden Markov models (HMM) and Gaussian mixture models (GMM). The nature of the input features and the topology of the HMMs have been varied in order to analyze each one's influence. Results with respect to the phone boundary metric over a dataset of 385 hand-aligned utterances show that the network is mostly invariant to the input features, while regular HMM topologies do perform better in comparison to a modified version used in chain models. Conversely, the neural network still does not outperform GMM models for phonetic alignment.


Forced Phonetic Alignment in Brazilian Portuguese Using Time-Delay Neural Networks

This work presents a new database containing high sampling
rate recordings of a single male speaker reading sentences in Brazilian
Portuguese with neutral voice, along with the corresponding text corpus. Intended for synthesis and other speech-oriented applications, the
dataset contains text scripts extracted from a popular Brazilian news TV
program, read out loud by a trained individual in a controlled environment, resulting in roughly 20 h of audio data. The text was normalized
in the recording process and special textual occurrences (e.g. acronyms,
numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental
sounds and background noise has been kept to a minimum in all audio
samples. To illustrate the potential benefits of having this data available,
text-to-speech experiments were conducted using state-of-the-art models
for speech synthesis (Tacotron 2 and Waveglow). As a result, we obtained
intelligible and natural sounding voices from as few as 8 minutes of audio samples coming from an unseen target speaker, after having trained
over our data; moreover, by increasing the target recording time to 75
minutes, we have noticeably improved accuracy in pronunciation.


A Corpus of Neutral Voice Speech in Brazilian Portuguese

Paraphrasing is a fundamental technique for many text applications. Typically, this task is performed through models that perform
lexical and translation operations, which tend to present a trade-off between meaning preservation and diversity. In this paper, we present a
transformer-based approach called PTT5-Paraphraser, a PPT5 model
fine-tuned on TaPaCo, a large corpus of paraphrases. PTT5-Paraphraser
achieves good results according to a number of metrics, showing a good
compromise between diversity and fidelity to the original meaning. Two
human evaluations are made to explore the paraphrases produced by our
model: the first analyzes their quality in terms of preserving the meaning
and diversity of sentences, while the second compares automatically generated paraphrases with human-made ones. Finally, we perform a classification task, which shows that datasets augmented with paraphrases
can substantially increase the performance of classifiers.

PTT5-Paraphraser: Diversity and Meaning Fidelity in Automatic Portuguese Paraphrasing

This work presents an analysis of Brazilian political discourse from speeches and social media posts, focusing on the ability to transfer learned models’ knowledge between different contexts. The analysis is conducted through PoliS, a new resource containing two datasets of political discussions labeled for party and ideological leaning from congressional speeches and social media posts by political influencers. The transfer learning experiments are performed using the transcripts of the congressional speeches to train a model used to predict the political leaning of social media influencers. To evaluate the robustness of the model, the analysis includes a time-lag study of the performance degradation of the transferred model. We find that relatively little social media data (about one hour) is needed to achieve reasonable performance in classification, and that performance does not degrade significantly over time.

A transfer learning analysis of political leaning classification in cross-domain content

Legal text processing is a challenging task for modeling approaches due to the peculiarities inherent to its features, such as long texts and their technical vocabulary. Topic modeling consists of discovering a semantic structure in the text. This paper investigates the application of topic modeling and the use of information about the legislation cited in identifying the subject of legal documents and evaluating its applicability in the classification of Brazilian lawsuits. The models were trained with a Golden Collection of 16 thousand initial petitions and indictments from the Court of Justice of the State of Ceará, in Brazil, whose lawsuits were classified in the five more representative National Council of Justice (CNJ) of Brazil classes - Common Civil Procedure, Execution of Extrajudicial Title, Criminal Action - Ordinary Procedure, Special Civil Court Procedure, and Tax Enforcement. The results obtained outperform the baseline, achieving 0.89 of F1 score (macro). Our interpretation is that the representation of the document through contextual embeddings generated by BERT, as well as the architecture of the model with bidirectional contexts, makes it possible to capture the specific context of the domain of legal documents. Thus, the use of the legislation mentioned in the representation of documents can improve the accuracy of the classification task.


Using Topic Modeling in Classification of Brazilian Lawsuits

Even though most of its energy generation comes from renewable sources, Brazil is one of the largest emitters of greenhouse gases in the world, due to intense farming and deforestation of biomes such as the Amazon Rainforest, whose preservation is essential for compliance with the Paris Agreement. Still, regardless of lobbies or prevailing political orientation, all government legal actions are published daily in the Brazilian Federal Official Gazette (BFOG, or ``Diário Oficial da União" in Portuguese). However, with hundreds of decrees issued every day by the authorities, it is absolutely burdensome to manually analyze all these processes and find out which ones can pose serious environmental hazards. In this paper, we present a strategy to compose automated techniques and domain expert knowledge to process all the data from the BFOG. We also provide the Government Actions Tracker, a highly curated dataset, in Portuguese, annotated by domain experts, on federal government acts about the Brazilian environmental policies. Finally, we build and compared four different NLP models on the classfication task in this dataset. Our best model achieved a F1-score of $0.714 \pm 0.031$. In the future, this system should serve to scale up the high-quality tracking of all oficial documents with a minimum of human supervision and contribute to increasing society's awareness of government actions.

Tracking Environmental Policy Changes in the Brazilian Federal Official Gazette

The anchorage to real data is one of the main parameters that guarantees the quality and the coverage of lexical resources, especially in the context of specialized domains. Thus, lexicon extraction from corpora is a consensual method for building lexical resources. However, given that data validation by experts in specialized contexts is a necessary step, the automatic screening of data becomes fundamental to maximize the informational value of the interaction with experts. In this presentation we present and discuss a hybrid methodology, combining linguistic and statistical approaches, focusing on the extraction of specialized lexical units and salient semantic information using CQL grammars. The proposed method involves several steps, from frequency information analyses, concordances and collocations extraction to manual revision and expert validation and encompasses the construction and application of knowledge-based patterns CQL grammars. We present two CQL grammars for lexical and semantic information extraction developed for Portuguese and Italian and evaluate results from its application to specialized corpora on Public Art domain, demonstrating the value of this method for lexicon and semantic information extraction from large data.

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Challenges in annotating a treebank of clinical narratives in Brazilian Portuguese

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