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This paper presents a technique that employs linguistic resources to refine PoS tagging using the Universal Dependencies (UD) model.
The technique is based on the development and use of lists of non-ambiguous single tokens and non-ambiguous co-occuring tokens in Portuguese (regardless of whether they constitute multiword expressions or not). These lists are meant to automatilly correct the tags for such tokens after tagging. The technique is applied over the output of two well-known state of the art systems - UDPipe and UDify - and the results for a real data set have shown a significant improvement of annotation accuracy. Overall, we improve tagging accuracy by up to 1.4%, and, in terms of the number of fully correct tagged sentences, our technique produces results that are 13.9% more accurate than the corresponding original system.

BRACIS 2021

Universal Dependencies-based PoS Tagging Refinement through Linguistic Resources

linguistic resource

universal dependencies

pos tagging

**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**.

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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

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of 85.11%, Sensitivity of 85.25%, Specificity of 85.16%, F1-score of 85.03%, and an AUC of 0.9758.

Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays

Computational determination of protein stability upon point mutations is very useful in a wide field of applications. Reliability of such computational predictions is essential. Unfortunately, existing computational tools frequently disagree in their results. In the present study, the usage of Ensemble Learning Algorithms to aggregate the results from different stability prediction tools is investigated. Techniques of Stacking, Bagging, and Boosting as well as different Machine Learning algorithms as combiner function are explored. All the investigation is carried out in real dataset ProTherm for which experimental results are known. The proposed methodology was validated considering two different experiments according to the training set. Results show that our proposed ensemble approach is appropriate to predict the effect of point mutations on protein stability showing more reliable results than the individual tools improving overall accuracy, precision and/or recall.

Ensemble of Protein Stability Upon Point Mutation Predictors

Family farming represents a critical segment of Brazilian agriculture, involving more than 5 million properties and generating 74% of rural jobs in the country. Yield losses caused by crop diseases and pests can be devastating for small-scale producers. However, successful disease control requires correct identication, which challenges smallholders, who often lack technical assistance. The present work proposes a system that detects disease symptoms in images of plant leaves to assist phytopathology experts. The objective is to decrease the experts' workload and enable consulting services for free or at nominal cost. In addition, the required digital communication channel will promote the formation of a caring community ready to offer unpaid advice for family farmers. The machine learning and renement of the assistance system are described in detail. The developed classication system achieves a recall value of 95%.

Supervised Training of a Simple DIgital Assistant for a Free Crop Clinic

Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user's interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.

Time-Dependent Item Embeddings for Collaborative Filtering

In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting to the user long lists of irrelevant items. Since innovations introduced by transformer-based models that have reached the state of the art performance in tasks involving natural language processing (NLP), the application of these models also in tasks involving code intelligence, such as code completion, has become a frequent object of study in recent years. In this paper, we present a transformer-based model trained on 1.2 million Java files gathered from top-starred Github repositories. Our evaluation approach was based on measuring the model's ability to predict the completion of a line, proposing a new metric to measure the applicability of the suggestions that we consider better adapted to the practical reality of the code completion task. With a recently developed Java web project as a test set, our experiments showed that in 55.9% of the test cases the model brought at least one suggestion applicable, while the best baseline model presented this in 26.5%.

Code autocomplete using Transformers

Time series classification has a wide variety of possible applications, like outlines of figure types, signs of movement and sensor signals. This diversity may present different results with the application of machine learning techniques. Among the different ways to classify time series, two, in particular, are explored in this paper: the shapelet primitive and the neural network classification. A shapelet is a sub-sequence of the time series symbolic, like a high-level descriptor, that are representative for the class to which they belong. These features act as common knowledge used by domain experts. This paper proposes a CNN training protocol to achieve better results in the classification of time series.
The idea consists of decomposing the original time series into shapelets and noise. Then, the shapelets are used to train a classifier while the no-shapelets (“signal noise”) is used to train another classifier.
The original time series is then used to train two final classifiers starting with the weights of shapelet and no-shapelet classifiers. This previous extraction of this representation can improve classification ability in a convolutional neural network using transfer learning. The experimental evaluation shows that the pre-selection of shapelets before the network training changes the classifier results for several databases and, consequently, improves classification accuracy.

Transfer Learning of Shapelets for Time Series Classification Using Convolutional Neural Network

Semantic textual similarity (STS) measures how semantically similar two sentences are. In the context of the Portuguese language, STS literature is still incipient but includes important initiatives like the ASSIN and ASSIN 2 shared tasks. The state-of-the-art
for those datasets is a contextual embedding produced by a Portuguese
pre-trained and fine-tuned BERT model. In this work, we investigate
the application of Sentence-BERT (SBERT) contextual embeddings to
these datasets. Compared to BERT, SBERT is a more computationally efficient approach, enabling its application to scalable unsupervised
learning problems. Given the absence of SBERT models pre-trained in
Portuguese and the computational cost for such training, we adopt multilingual models and also fine-tune them for Portuguese. Results showed
that SBERT embeddings were competitive especially after fine-tuning,
numerically surpassing the results of BERT on ASSIN 2 and the results
observed during the shared tasks for all datasets considered.

Comparing contextual embeddings for semantic textual similarity in Portuguese

In this paper, we present an efficient method for training models for speaker recognition using small or under-resourced datasets. This method requires less data than other SOTA (State-Of-The-Art) methods, e.g. the Angular Prototypical and GE2E loss functions, while achieving similar results to those methods. This is done using the knowledge of the reconstruction of a phoneme in the speaker's voice. For this purpose, a new dataset was built, composed of 40 male speakers, who read sentences in Portuguese, totaling approximately 3h. We compare the three best architectures trained using our method to select the best one, which is the one with a shallow architecture. Then, we compared this model with the SOTA method for the speaker recognition task: the Fast ResNet--34 trained with approximately 2,000 hours, using the loss functions Angular Prototypical and GE2E. Three experiments were carried out with datasets in different languages. Among these three experiments, our model achieved the second best result in two experiments and the best result in one of them. This highlights the importance of our method, which proved to be a great competitor to SOTA speaker recognition models, with 500x less data and a simpler approach.

Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models

Event Extraction (EE) is the task of identifying mentions of
particular event types and their arguments in text, and it constitutes an
important and challenging task within the area of Information Extraction
(IE). However, in the context of the Portuguese language, very little work
has been conducted on this topic. In this paper, we propose a neural-
based method for EE, as well as a data resource to mitigate this research
gap. We also present a data augmentation strategy for EE, employing
an Open Information Extraction (OIE) system, aiming to overcome the
shortage in annotated data for the problem in the Portuguese language.
Our experimental results show that our method is able to predict event
types and arguments automatically, and the proposed method of data
augmentation, in one of the two evaluated samples, contributes to the
performance of the tested models in the subtask of argument role pre-
diction. Further, an implementation of our method is available to the
community, as the models trained in our experiments.

Joint Event Extraction with Contextualized Word Embeddings for the Portuguese Language

Automatic Text Simplification (ATS) has played a significant role in the Natural Language Processing (NLP) field.
ATS is a sequence-to-sequence problem aiming to create a new version of the original text removing complex and domain-specific words. 
It can improve communication and understanding of documents from specific domains, as well as support second language learning. 
This paper presents an empirical study on the use of state-of-the-art ATS methods to simplify texts in Portuguese. It is important to remark that the literature reports the challenge in analyzing Portuguese texts due to the lack of resources compared to other languages (i.e., English).
More specifically, this work evaluated different Neural Machine Translation (NMT) techniques for ATS in Portuguese. The experiments showed that NMT achieved promising results in Portuguese texts, obtaining 40.89 BLEU score using multiple parallel corpora and raising the overall readability score by more than 5 points.

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Universal Dependencies-based PoS Tagging Refinement through Linguistic Resources

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