Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection

TL;DR

This paper presents a deep learning approach for irony detection in English tweets, combining dependency parsing, phrase embedding, and emotion prediction to improve classification accuracy.

Contribution

It introduces a novel system that integrates dependency parsing and LSTM-based embeddings for irony detection in social media texts.

Findings

Effective phrase-based representation of tweets

Improved irony classification accuracy

Combines dependency parsing with neural embeddings

Abstract

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.