Marcos André Gonçalves

Also published as: Marcos André Gonçalves


Evaluating Recognizing Question Entailment Methods for a Portuguese Community Question-Answering System about Diabetes Mellitus
Thiago Castro Ferreira | João Victor de Pinho Costa | Isabela Rigotto | Vitoria Portella | Gabriel Frota | Ana Luisa A. R. Guimarães | Adalberto Penna | Isabela Lee | Tayane A. Soares | Sophia Rolim | Rossana Cunha | Celso França | Ariel Santos | Rivaney F. Oliveira | Abisague Langbehn | Daniel Hasan Dalip | Marcos André Gonçalves | Rodrigo Bastos Fóscolo | Adriana Pagano
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach. Given a premise question, RQE aims to retrieve semantically similar, already answered, archived questions. We build a new Portuguese benchmark corpus with 785 pairs between premise questions and archived answered questions marked with relevance judgments by medical experts. Based on the benchmark corpus, we leveraged and evaluated several RQE approaches ranging from traditional information retrieval methods to novel large pre-trained language models and ensemble techniques using learn-to-rank approaches. Our experimental results show that a supervised transformer-based method trained with multiple languages and for multiple tasks (MUSE) outperforms the alternatives. Our results also show that ensembles of methods (stacking) as well as a traditional (light) information retrieval method (BM25) can produce competitive results. Finally, among the tested strategies, those that exploit only the question (not the answer), provide the best effectiveness-efficiency trade-off. Code is publicly available.


Combining Representations For Effective Citation Classification
Claudio Moisés Valiense de Andrade | Marcos André Gonçalves
Proceedings of the 8th International Workshop on Mining Scientific Publications

In this paper, we describe our participation in two tasks organized by WOSP 2020, consisting of classifying the context of a citation (e.g., background, motivational, extension) and whether a citation is influential in the work (or not). Classifying the context of an article citation or its influence/importance in an automated way presents a challenge for machine learning algorithms due to the shortage of information and inherently ambiguity of the task. Its solution, on the other hand, may allow enhanced bibliometric studies. Several text representations have already been proposed in the literature, but their combination has been underexploited in the two tasks described above. Our solution relies exactly on combining different, potentially complementary, text representations in order to enhance the final obtained results. We evaluate the combination of various strategies for text representation, achieving the best results with a combination of TF-IDF (capturing statistical information), LDA (capturing topical information) and Glove word embeddings (capturing contextual information) for the task of classifying the context of the citation. Our solution ranked first in the task of classifying the citation context and third in classifying its influence.


Analyzing the Dynamic Evolution of Hashtags on Twitter: a Language-Based Approach
Evandro Cunha | Gabriel Magno | Giovanni Comarela | Virgilio Almeida | Marcos André Gonçalves | Fabrício Benevenuto
Proceedings of the Workshop on Language in Social Media (LSM 2011)