# A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

arXiv: https://arxiv.org/abs/1912.07976 · HTML: https://mlsanity.com/paper/1912.07976/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 18 of 18)

- [Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining](https://mlsanity.com/paper/2204.12440/) (2022)
- [Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.10816/) (2021)
- [LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation](https://mlsanity.com/paper/2110.08604/) (2021)
- [Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild](https://mlsanity.com/paper/2101.09449/) (2021)
- [Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis](https://mlsanity.com/paper/2203.07090/) (2022)
- [PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2208.01368/) (2022)
- [AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations](https://mlsanity.com/paper/2108.11656/) (2021)
- [A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.11260/) (2023)
- [Aspect-Based Sentiment Analysis Techniques: A Comparative Study](https://mlsanity.com/paper/2407.02834/) (2024)
- [Re-Evaluating GermEval17 Using German Pre-Trained Language Models](https://mlsanity.com/paper/2102.12330/) (2021)
- [A new hazard event classification model via deep learning and multifractal](https://mlsanity.com/paper/2209.05263/) (2022)
- [Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews](https://mlsanity.com/paper/2408.13202/) (2024)
- [Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments](https://mlsanity.com/paper/2312.12253/) (2023)
- [Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Information](https://mlsanity.com/paper/2409.14997/) (2024)
- [Explicit Interaction Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2106.11148/) (2021)
- [Knowledge-Aware Neural Networks for Medical Forum Question Classification](https://mlsanity.com/paper/2109.13141/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies](https://mlsanity.com/paper/2509.16788/) (2025)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the existing work focuses on the subtask of aspect term polarity inferring and ignores the significance of aspect term extraction. Besides, the existing researches do not pay attention to the research of the Chinese-oriented ABSA task. Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC. Compared with existing models, this model equips the capability of extracting aspect term and inferring aspect term polarity synchronously, moreover, this model is effective to analyze both Chinese and English comments simultaneously and the experiment on a multilingual mixed dataset proved its availability. By integrating the domain-adapted BERT model, the LCF-ATEPC model achieved the state-of-the-art performance of aspect term extraction and aspect polarity classification in four Chinese review datasets. Besides, the experimental results on the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets outperform the state-of-the-art performance on the ATE and APC subtask.