Marco de Gemmis


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SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networks
Marco Polignano | Marco de Gemmis | Giovanni Semeraro
Proceedings of the 13th International Workshop on Semantic Evaluation

Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features. Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence. Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated. Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task. The final system configuration achieved a micro F1 score of 0.7089. The python code of the system is fully available at


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Combining Knowledge-based Methods and Supervised Learning for Effective Italian Word Sense Disambiguation
Pierpaolo Basile | Marco de Gemmis | Pasquale Lops | Giovanni Semeraro
Semantics in Text Processing. STEP 2008 Conference Proceedings


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UNIBA: JIGSAW algorithm for Word Sense Disambiguation
Pierpaolo Basile | Marco de Gemmis | Anna Lisa Gentile | Pasquale Lops | Giovanni Semeraro
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)