Maoquan Wang


2018

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EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words
Shiyun Chen | Maoquan Wang | Liang He
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurrent neural network which has achieved satisfactory performs in Natural Language processing. Considering the text comes from social media, it contains many discrepant abbreviations and online terms, we also combine word-level and character-level word vector embedding to better handling the words not appear in the vocabulary. Our single model1 achieved 29.50% Macro F-score in test data and ranks 9th among 48 teams.

2017

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EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering
Yufei Xie | Maoquan Wang | Jing Ma | Jian Jiang | Zhao Lu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50%.

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EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification
Maoquan Wang | Shiyun Chen | Yufei Xie | Lu Zhao
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative.