# Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision

arXiv: https://arxiv.org/abs/1907.03750 · HTML: https://mlsanity.com/paper/1907.03750/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2019.

## Built on by (most cited first, 25 of 25)

- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2107.12214/) (2021)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge](https://mlsanity.com/paper/2105.02520/) (2021)
- [PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2110.04794/) (2021)
- [Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2106.03315/) (2021)
- [Aspect Sentiment Triplet Extraction Using Reinforcement Learning](https://mlsanity.com/paper/2108.06107/) (2021)
- [First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2102.08549/) (2021)
- [STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2211.15003/) (2022)
- [A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task](https://mlsanity.com/paper/2103.15255/) (2021)
- [MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2406.11234/) (2024)
- [SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2302.13032/) (2023)
- [CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2310.15577/) (2023)
- [Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)](https://mlsanity.com/paper/2103.05815/) (2021)
- [AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2211.03837/) (2022)
- [A semantically enhanced dual encoder for aspect sentiment triplet extraction](https://mlsanity.com/paper/2306.08373/) (2023)
- [iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples](https://mlsanity.com/paper/2311.03896/) (2023)
- [Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning](https://mlsanity.com/paper/2403.07342/) (2024)
- [Explicit Interaction Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2106.11148/) (2021)
- [Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2108.05098/) (2021)
- [Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction](https://mlsanity.com/paper/2109.03821/) (2021)
- [Aspect-Sentiment-Multiple-Opinion Triplet Extraction](https://mlsanity.com/paper/2110.07303/) (2021)
- [Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis](https://mlsanity.com/paper/2311.16678/) (2023)
- [MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2607.08080/) (2026)

## Abstract (arXiv)

Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an algorithm to automatically mine extraction rules from existing training examples based on dependency parsing results. The mined rules are then applied to label a large amount of auxiliary data. Finally, we study training procedures to train a neural model which can learn from both the data automatically labeled by the rules and a small amount of data accurately annotated by human. Experimental results show that although the mined rules themselves do not perform well due to their limited flexibility, the combination of human annotated data and rule labeled auxiliary data can improve the neural model and allow it to achieve performance better than or comparable with the current state-of-the-art.