Brazil

In the hospital environment, the incidence of adverse events (unforeseen incidents that cause harm to patients) is the primary concern of risk management teams. The use of machine learning techniques may help healthcare professional to identify and mitigate adverse events. This thesis develops two experiments to evaluate machine learning approaches to identify two major adverse events in electronic health records. In this extended abstract we cover the algorithm created to identify fall events in clinical notes, as they are closely related to the processing of portuguese language. We annotated 1,402 clinical sentences with fall events to train a Token Classifier (TkC) to detect words within the context of falls. The TkC was able to correctly identify 85% of the sentences with fall events.

PROPOR 2022

Applying Machine Learning to Electronic Health Records: a Study on Two Adverse Events

electronic health records

adverse events

supervised learning

machine learning

unsupervised learning

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.

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

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. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

poster

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.

Drilling Lexico-Semantic Knowledge in Portuguese from BERT

Named Entity Recognition (NER) is the task of identifying textual elements and categorizing them into predefined classes, such as names of people, locations, organizations, and others. Recently, NER systems based on neural networks have obtained state-of-the-art results. One of the main components of this kind of system is the word embeddings representation adopted that can be static (traditional) or contextual trained in language models. This work analyzes the strategy of combining traditional and contextual embeddings to obtain richer representations for Portuguese NER. We adopt a Bidirectional Long ShortTerm Memory (BiLSTM) as a classification model integrated with the Conditional Random Fields (CRF) algorithm. Experiments were carried out in different corpora, and the results obtained demonstrate that the combination strategy of word embeddings models is viable and led to results comparable with state-of-the-art NER systems in Portuguese.

Combining Word Embeddings for Portuguese Named Entity Recognition

Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages. This lack of labeled data has led to the rise of low-resource methods, that assume low data availability in natural language processing. Among them, zero-shot learning stands out, which consists of learning a classifier without any previously labeled data. The best results reported with this approach use language models such as Transformers, but fall into two problems: high execution time and inability to handle long texts as input. This paper proposes a new model, ZeroBERTo, which leverages an unsupervised clustering step to obtain a compressed data representation before the classification task. We show that ZeroBERTo has better performance for long inputs and shorter execution time, outperforming XLM-R by about 12% in the F1 score in the FolhaUOL dataset.

ZeroBERTo - Leveraging Zero-Shot Text Classification by Topic Modeling

Our contribution presents the first ever stance and sentiment annotated corpus in Portuguese. Using data from Twitter, we annotated a data set with over 15,000 tweets. In building the corpus, we made both random and supervised searches to maximize balance. Using four annotators, we have classified each tweet on four categories: support, against, neutral or inconclusive. Furthermore, we have annotated each tweet for sentiment: positive, negative, neutral, sarcasm or inconclusive. Our annotators yield strong inter-annotator agreement. In addition, we test a baseline model using a pre-trained BERT model for Portuguese. Results suggest that our data set is in line with its counterparts in other languages.

SS-PT: A Stance and Sentiment data set from Portuguese quoted tweets

This paper presents a targeted syntactic evaluation of Transformer models for Galician-Portuguese. We defined three experiments that allow to explore how these models, trained with a masked language modeling objective, encode syntactic knowledge. To do so, we created a new dataset including test instances of number (subject-verb), gender (subject-predicative adjective), and person (subject-inflected infinitive) agreement. This dataset was used to evaluate monolingual and multilingual BERT models, controlling for various aspects such as the presence of attractors or the distance between the dependent elements. The results show that Transformer models perform competently in many cases, but they are generally confounded by the presence of attractors in long-distance dependencies. Moreover, the different behavior of monolingual models trained with the same corpora reinforces the need for a deep exploration of the network architectures and their learning process.

A Targeted Assessment of the Syntactic Abilities of Transformer Models for Galician-Portuguese

This paper describes the development of the first test suite for the language direction Portuguese-English. Designed for fine-grained linguistic analysis, the test suite comprises 330 test sentences for 66 linguistic phenomena and 14 linguistic categories. Eight different MT systems were compared using quantitative and qualitative methods via the test suite: DeepL, Google Sheets, Google Translator, Microsoft Translator, Reverso, Systran, Yandex and an internally built NMT system
trained over 30 hours on 2,5M sentences. It was found that ambiguity, named entity & terminology and verb valency are the categories where MT systems struggle most. Negation, pronouns, subordination, verb tense/aspect/mood and false friends are the categories where MT systems perform best.

A test-suite for the evaluation of Portuguese-English machine translations

This work focuses on the enrichment of existing Portuguese word embeddings with visual information. The combined text-image embeddings were tested against their text-only counterparts in common NLP tasks. The new embeddings were tested in two different domains - general news and a geosciences. The results show an increase in performance for several tasks, which indicates that visual information fusion for word embeddings can be useful for certain tasks.

Enriching Portuguese Word Embeddings with Visual Information

Abstract Meaning Representation (AMR) is a semantic formalism that has been widely adopted in the area of semantic parsing. We present in this paper our contribution to the task for Portuguese. We investigated semantic parsing methods of different paradigms, producing state-of-the-art results for this language. We also introduced the first AMR-annotated corpus for Portuguese, a novel and better semantic parsing evaluation measure, and a new AMR-based alignment method.

Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Languages evolve internally and externally in relation to themselves and to others. It is for this reason that the automatic calculation of the historical and current distance between languages from corpora is an interesting challenge. In this thesis we have developed a methodology for calculating this distance from perplexity, a metric that has also proved to be suitable for automatic identification between languages. We have also tested the role that orthography plays in the distance between languages. Finally, we have created an adhoc corpus called Carvalho in orthographies as close as possible to the original ones, where we have carried out our experiments.

Corpus-based measures of distance between languages and variants. Application to the historical linguistics of Galician, Portuguese, Spanish and English

In the legal domain, documents have peculiarities related to their length, specialized use, and vocabulary. Recent neural network-based approaches can generate high-quality summaries. However, these approaches have been used mostly for creating concise abstracts for news articles in English and suffer from the "hallucination", i.e. the inclusion of extraneous material that is not supported by the original text. 
In this work, we propose LegalSumm, a method for summarizing legal texts in Portuguese. Our solution relies on Transformers and textual entailment is used to reduce hallucinations. To cope with long documents, we propose different chunking strategies. We compared LegalSumm to strong extractive and abstractive summarization baselines and collected the opinion of legal experts on the automatically generated summaries. The results show that LegalSumm outperforms existing summarization methods.

Applying Machine Learning to Electronic Health Records: a Study on Two Adverse Events

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Drilling Lexico-Semantic Knowledge in Portuguese from BERT

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