Eugénio Oliveira


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FinDSE@FinTOC-2019 Shared Task
Carla Abreu | Henrique Cardoso | Eugénio Oliveira
Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)


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FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System
Carla Abreu | Eugénio Oliveira
Proceedings of The 12th International Workshop on Semantic Evaluation

We present the approach developed at the Faculty of Engineering of the University of Porto to participate in SemEval-2018 Task 5: Counting Events and Participants within Highly Ambiguous Data covering a very long tail.The work described here presents the experimental system developed to extract entities from news articles for the sake of Question Answering. We propose a supervised learning approach to enable the recognition of two different types of entities: Locations and Participants. We also discuss the use of distance-based algorithms (using Levenshtein distance and Q-grams) for the detection of documents’ closeness based on the entities extracted. For the experiments, we also used a multi-agent system that improved the performance.


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FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
Pedro Saleiro | Eduarda Mendes Rodrigues | Carlos Soares | Eugénio Oliveira
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News. The task consisted in predicting a real continuous variable from -1.0 to +1.0 representing the polarity and intensity of sentiment concerning companies/stocks mentioned in short texts. We modeled the task as a regression analysis problem and combined traditional techniques such as pre-processing short texts, bag-of-words representations and lexical-based features with enhanced financial specific bag-of-embeddings. We used an external collection of tweets and news headlines mentioning companies/stocks from S&P 500 to create financial word embeddings which are able to capture domain-specific syntactic and semantic similarities. The resulting approach obtained a cosine similarity score of 0.69 in sub-task 5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines.


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Exploring the Vector Space Model for Finding Verb Synonyms in Portuguese
Luís Sarmento | Paula Carvalho | Eugénio Oliveira
Proceedings of the International Conference RANLP-2009