# A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining

arXiv: https://arxiv.org/abs/2010.05357 · HTML: https://mlsanity.com/paper/2010.05357/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models](https://mlsanity.com/paper/2307.05646/) (2023)
- [ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect Sentiment in Dialogues](https://mlsanity.com/paper/2403.05326/) (2024)
- [KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs](https://mlsanity.com/paper/2501.03560/) (2025)

## Abstract (arXiv)

Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach.