Jinghang Gu


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PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)
Rong Xiang | Jinghang Gu | Emmanuele Chersoni | Wenjie Li | Qin Lu | Chu-Ren Huang
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context. Our top system, based on a combination of lexical, syntactic, word embeddings and Transformers-derived features and on a Gradient Boosting Regressor, achieves a top correlation score of 0.754 on the subtask 1 for single words and 0.659 on the subtask 2 for multiword expressions.


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Affection Driven Neural Networks for Sentiment Analysis
Rong Xiang | Yunfei Long | Mingyu Wan | Jinghang Gu | Qin Lu | Chu-Ren Huang
Proceedings of the 12th Language Resources and Evaluation Conference

Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0% to 1.5% in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.