Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection
This work addresses irony detection for natural language processing applications, but it is incremental as it builds on existing parsing and deep learning techniques.
The paper tackled irony detection in English tweets by breaking tweets into phrases using a dependency parser and using an LSTM-based model pre-trained on emoticon prediction for classification, achieving results in the SemEval 2018 competition.
In this paper, we describe the system submitted for the SemEval 2018 Task 3 (Irony detection in English tweets) Subtask A by the team Binarizer. Irony detection is a key task for many natural language processing works. Our method treats ironical tweets to consist of smaller parts containing different emotions. We break down tweets into separate phrases using a dependency parser. We then embed those phrases using an LSTM-based neural network model which is pre-trained to predict emoticons for tweets. Finally, we train a fully-connected network to achieve classification.