Brazil

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.


PROPOR 2022

Using Topic Modeling in Classification of Brazilian Lawsuits

brazilian lawsuits

legal domain

topic modeling

text classification

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

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.

Automatic book genre classification is a hard task as it requires the whole book's content or a high-quality summary, which is challenging to write automatically. On the other hand, online reviews are an accessible resource for readers to evaluate a book or even get a general sense about it, including its genre. As the amount of book reviews is always increasing, using such information to genre classification needs a robust solution to deal with high volumes of data. In such a context, we introduce a model for automatically classifying book genres by analyzing online text reviews. We build a dataset of compiled texts from online book reviews. Then, we use multiple machine learning algorithms to categorize a book into a specific genre. Such a process enables to compare algorithms and detect the best classifiers. Hence, the most efficient machine learning algorithm completed the task with an accuracy of 96%; i.e., the proposed model is convenient for various information retrieval systems due to its high certainty and efficiency.

Book Genre Classification Based on Reviews of Portuguese-language Literature

The amount of legislative documents produced within the past decade has risen dramatically, making it diﬃcult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8% in the analysis by categories and 81.04% in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were “FUNDlei” and “DATA”, and the ones with the worst results were “EVENTO” and “PESSOAgrupoind”. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89% in the analysis by categories and 59.67% in the analysis by types.

UlyssesNER-Br: a corpus of Brazilian Legislative Documents for Named Entity Recognition

We present PetroBERT, a BERT model adapted to the oil and gas domain.

PetroBERT: a domain adaptation language model for oil and gas applications in Portuguese

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.

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