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Deep learning models have been the state-of-the-art for a variety of challenging tasks in natural language processing, but to achieve good results they often require big labeled datasets. Deep active learning algorithms were designed to reduce the annotation cost for training such models. Current deep active learning algorithms, however, aim at training a good deep learning model with as little labeled data as possible, and as such are not useful in scenarios where the full dataset must be labeled. As a solution to this problem, this work investigates deep active-self learning algorithms that employ self-labeling using the trained model to help alleviate the cost of annotating full datasets for named entity recognition tasks. The experiments performed indicate that the proposed deep active-self learning algorithm is capable of reducing manual annotation costs for labeling the complete dataset for named entity recognition with less than 2\% of the self labeled tokens being mislabeled. We also investigate an early stopping technique that doesn&#39;t rely on a validation set, which effectively reduces even further the annotation costs of the proposed active-self learning algorithm in real world scenarios.

BRACIS 2021

Deep Active-Self Learning Applied to Named Entity Recognition

self learning

named entity recognition

active learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

Deep learning models have been the state-of-the-art for a variety of challenging tasks in natural language processing, but to achieve good results they often require big labeled datasets. Deep active learning algorithms were designed to reduce the annotation cost for training such models. Current deep active learning algorithms, however, aim at training a good deep learning model with as little labeled data as possible, and as such are not useful in scenarios where the full dataset must be labeled. As a solution to this problem, this work investigates deep active-self learning algorithms that employ self-labeling using the trained model to help alleviate the cost of annotating full datasets for named entity recognition tasks. The experiments performed indicate that the proposed deep active-self learning algorithm is capable of reducing manual annotation costs for labeling the complete dataset for named entity recognition with less than 2\% of the self labeled tokens being mislabeled. We also investigate an early stopping technique that doesn't rely on a validation set, which effectively reduces even further the annotation costs of the proposed active-self learning algorithm in real world scenarios.

technical paper

**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

General and adaptive strategies have been a highly pursued goal of the optimization community, due to the domain-dependent set of configurations (operators and parameters) that is usually required for achieving high quality solutions. This work investigates a Deep Q-Network (DQN) selection strategy under an online selection Hyper-Heuristic algorithm and compares it with two state-of-the-art Multi-Armed Bandit (MAB) approaches. We conducted the experiments on all six problem domains from the HyFlex Framework. With our definition of state representation and reward scheme, the DQN was able to quickly identify the good and bad operators, which resulted on better performance than the MAB strategies on the problem instances that a more exploitative behavior deemed advantageous.


Online selection of heuristic operators with Deep Q-Netowrk: a study on the HyFlex Framework

Hyper-heuristics (HHs) are algorithms suitable for designing heuristics. HHs perform the search divided in two levels: they look for heuristic components in the high level and the heuristic is used, in the low level, to solve a set of instances of one or more problems. Different from offline HHs, hyper-heuristics with dynamic learning select or generate heuristics during the search. This paper proposes a hyper-heuristic associated with a dynamic learning strategy for selecting Iterated Greedy (IG) components. The proposal is capable of selecting appropriate values for six IG components: local search, perturbation, destruction size, neighborhood size, destruction position and local search focus. The proposed HH is tested with six dynamic adaptation strategies: random, e-greedy, probability matching, multi-armed bandit, LinUCB, and Thompson Sampling (TS). The hyper-parameters of each strategy are tuned by irace. As a testbed, we use several instances with four different sizes (20, 50, 100 and 200 jobs) of three different formulations of flowshop problems (permutation, no-wait, no-idle), two distinct objectives (makespan, flowtime), and four processing time distributions (uniform, exponential and job or machine correlated). The results show that different strategies are most suitable for adapting different IG components, TS performs quite well for all components and, except for local search, using adaptation is always beneficial when compared with the IG running with standard parameters.

Dynamic Learning in Hyper-Heuristics to Solve Flowshop Problems

Gene Regulatory Networks (GRNs) inference from gene expression data is a hard task and a widely addressed challenge. GRNs can be represented as Boolean models similarly to digital circuits. Cartesian Genetic Programming (CGP), often used for designing circuits, can thus be adopted in the inference of GRNs. The main CGP operator for generating candidate designs is mutation, making its choice important for obtaining good results. Although there are many mutation operators for CGP, to the best of our knowledge, there is no analysis of them in the GRN inference problem. An evaluation of the Single ActiveMutation (SAM) and the Semantically-Oriented Mutation Operator (SOMO)is performed here for GRNs inference. Also, a combination of both operators is proposed. We use a benchmark single-cell RNA-Sequencingtime series data and its evaluation pipeline to measure the performance of the approaches. The experiments indicate that (i) combining SOMOand SAM provides the best results, and (ii) the results obtained by the proposal are competitive with those from state-of-the-art methods.

On the Analysis of CGP Mutation Operators when Inferring Gene Regulatory Networks using scRNA-Seq Time Series Data

This work focuses on the enrichment of existing Portuguese word embeddings with visual information in the form of visual embeddings. This information was extracted from images portraying given vocabulary terms and imagined visual embeddings learned for terms with no image data. These enriched embeddings were tested against their text-only counterparts in common NLP tasks. The results show an increase in performance for several tasks, which indicates that visual information fusion for word embeddings can be useful for word embedding based NLP tasks.

Enriching Portuguese Word Embeddings with Visual Information

Several coarsening algorithms have been developed as a powerful strategy to deal with difficult machine learning problems represented by large-scale networks, including, network visualization, trajectory mining, community detection and dimension reduction. It iteratively reduces the original network into a hierarchy of gradually smaller informative representations. However, few of these algorithms have been specifically designed to deal with bipartite networks and they still face theoretical limitations that need to be explored. Specifically, a recently introduced algorithm, called MLPb, is based on a synchronous label propagation strategy. In spite of an interesting approach, it presents the following two problems: 1) A high-cost search strategy in dense networks and 2) the cyclic oscillation problem yielded by the synchronous propagation scheme. In this paper, we address these issues and propose a novel fast coarsening algorithm more suitable for large-scale bipartite networks. Our proposal introduces a semi-synchronous strategy via cross-propagation, which allows a time-effective implementation and deeply reduces the oscillation phenomenon. The empirical analysis in both synthetic networks and real-world networks shows that our coarsening strategy outperforms previous approaches regarding accuracy and runtime.

Coarsening Algorithm via Semi-Synchronous Label Propagation for Bipartite Networks

As changes in external environments are inevitable, a lifelong mapping system is desirable for autonomous robots that aim at long-term operation. Capturing external environment changes into internal representations (for example, maps) is crucial for proper behavior and safety, especially in the case of autonomous vehicles. In this work, we propose a new large-scale mapping system for our autonomous vehicle or any other. The new mapping system is based on the Graph SLAM algorithm, with extensions to deal with the calibration of odometry directly in the optimization of the graph and to address map merging for long-term map maintenance. The mapping system can use sensor data from one or more robots to build and merge different types of occupancy grid maps. The system's performance is evaluated in a series of experiments carried out with data captured in complex real-world scenarios. The experimental results indicate that the new large-scale mapping system can provide high-quality occupancy grid maps for later navigation and localization of autonomous vehicles.

Long-term Map Maintenance in Complex Environments

In this paper, we present a new corpus for Rhetorical Role Identification in Portuguese legal documents. The corpus comprises petitions from 70 civil lawsuits filed in TJMS court and was manually labeled with rhetorical roles specifically tailored for petitions. Since petition documents are created without a standard structure, we had to deal with several issues to clean the extracted textual content. We assessed classic and deep learning machine learning methods on the proposed corpus.The best performing method obtained a F-score of 80.50. At the best of our knowledge, this is the first work to deal with rhetorical role identification for petitions, given that previous works focused only on judicial decisions. Additionally, it is also the first work to tackle this task for the Portuguese language. The proposed corpus, as well the proposed rhetorical roles, can foster new research in the judicial area and also lead to new solutions to improve the flow of Brazilian court houses.

Rhetorical Role Identification for Portuguese Legal Documents

Improving the interaction between consumers and marketplaces,
focusing on reaching higher conversion rates is one of the main
goals of e-commerce companies. Offering better results for user queries is
mandatory to improve user experience and convert it into purchases. This
paper investigates how named entity recognition can extract relevant attributes
from product titles to derive better lters for user queries. We
conducted several experiments based on MITIE and BERT applied to
smartphones/cellphones product titles from the largest Brazilian retail ecommerce.
Both of our strategies achieve outstanding results with a general
F1 score of around 95%.We concluded that using a classical machine
learning pipeline is still more useful than relying on large pre-trained language
models, considering the model's throughput and efficiency. Future
work may focus on evaluating the scalability and reusability capacity of
both approaches.

Named Entity Recognition for Brazilian Portuguese Product Titles

In the different areas of knowledge, textual data are important sources of information. This way, Information Extraction methods have been developed to identify and structure information present in textual documents. In particular there is the Named Entity Recognition (NER) task, which consists of using methods to identify Named Entities, such as Person, Place, among others, in texts, using techniques from Natural Language Processing and Machine Learning. Recent works explored the use of external sources of knowledge to boost the Machine Learning models with sets of domain specific relevant information for the NER task. This work aims to evaluate the aggregation of external knowl edge, in the form of Gazetter and Knowledge Graphs, for NER task. Our approach is composed of two steps: i) generation of embeddings, ii) defi nition and training of the Machine Learning methods. The experiments were conducted on four English datasets, and their results show that the applied strategies for external knowledge integration did not bring great gains to the models, as expressed by F1-Score metric. In the performed experiments, there was an F1-score increase in 17 of the 32 cases where external knowledge was used, but in most cases the gains were lesser than 0.5% in F1-score. In some scenarios the aggregated external knowledge does not capture relevant content, thus not being necessarily beneficial to the methodology.

When external knowledge does not aggregate in Named Entity Recognition

Competitive intelligence (CI) is a relevant area of a corporation and can support the strategic business area by showing those responsible, helping decision-making on how to position an organization in the market. This work uses the Bidirectional Transformer Encoding Representations (BERT) to process a sentence and its named entities and extract the parts of the sentences that represent or describe the semantic relationship between these named entities. The approach was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations without using other lexical-semantic resources. The results of the experiments show a precision of 73.5\% using the Jaccard metric that measures the similarity between sentences. The second contribution of this work is the manually constructed dataset with more than 4.500 tuples (phrase, entity, entity) annotated.

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