Evandro B. Fonseca

Also published as: Evandro Fonseca


2024

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Blip Copilot: a smart conversational assistant
Evandro Fonseca | Tayane Soares | Dyovana Baptista | Rogers Damas | Lucas Avanco
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2

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Autopilot: a smart sales assistant
Amanda Oliveira | João Alvarenga | Evandro Fonseca | William Colen
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2

2016

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Adapting an Entity Centric Model for Portuguese Coreference Resolution
Evandro Fonseca | Renata Vieira | Aline Vanin
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents the adaptation of an Entity Centric Model for Portuguese coreference resolution, considering 10 named entity categories. The model was evaluated on named e using the HAREM Portuguese corpus and the results are 81.0% of precision and 58.3% of recall overall, the resulting system is freely available

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Summ-it++: an Enriched Version of the Summ-it Corpus
Evandro Fonseca | André Antonitsch | Sandra Collovini | Daniela Amaral | Renata Vieira | Anny Figueira
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents Summ-it++, an enriched version the Summ-it corpus. In this new version, the corpus has received new semantic layers, named entity categories and relations between named entities, adding to the previous coreference annotation. In addition, we change the original Summ-it format to SemEval

2014

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Comparative Analysis of Portuguese Named Entities Recognition Tools
Daniela Amaral | Evandro Fonseca | Lucelene Lopes | Renata Vieira
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes an experiment to compare four tools to recognize named entities in Portuguese texts. The experiment was made over the HAREM corpora, a golden standard for named entities recognition in Portuguese. The tools experimented are based on natural language processing techniques and also machine learning. Specifically, one of the tools is based on Conditional random fields, an unsupervised machine learning model that has being used to named entities recognition in several languages, while the other tools follow more traditional natural language approaches. The comparison results indicate advantages for different tools according to the different classes of named entities. Despite of such balance among tools, we conclude pointing out foreseeable advantages to the machine learning based tool.

2013

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Geração de features para resolução de correferência: Pessoa, Local e Organização (Feature Generation for Coreference Resolution: Person, Location and Organization) [in Portuguese]
Evandro B. Fonseca | Renata Vieira | Aline A. Vanin
Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology