# A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection

arXiv: https://arxiv.org/abs/2311.09708 · HTML: https://mlsanity.com/paper/2311.09708/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Self-training Improves Pre-training for Natural Language Understanding](https://mlsanity.com/paper/2010.02194/) (2020)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Simple and Effective Paraphrastic Similarity from Parallel Translations](https://mlsanity.com/paper/1909.13872/) (2019)
- [Open Subtitles Paraphrase Corpus for Six Languages](https://mlsanity.com/paper/1809.06142/) (2018)
- [Embarrassingly Simple Unsupervised Aspect Extraction](https://mlsanity.com/paper/2004.13580/) (2020)
- [A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection](https://mlsanity.com/paper/2009.09107/) (2020)
- [Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training](https://mlsanity.com/paper/1909.00415/) (2019)
- [Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding](https://mlsanity.com/paper/2010.06705/) (2020)

## Built on by (most cited first, 1 of 1)

- [Improving Table Understanding with LLMs and Entity-Oriented Search](https://mlsanity.com/paper/2508.17028/) (2025)

## Abstract (arXiv)

Our work addresses the problem of unsupervised Aspect Category Detection using a small set of seed words. Recent works have focused on learning embedding spaces for seed words and sentences to establish similarities between sentences and aspects. However, aspect representations are limited by the quality of initial seed words, and model performances are compromised by noise. To mitigate this limitation, we propose a simple framework that automatically enhances the quality of initial seed words and selects high-quality sentences for training instead of using the entire dataset. Our main concepts are to add a number of seed words to the initial set and to treat the task of noise resolution as a task of augmenting data for a low-resource task. In addition, we jointly train Aspect Category Detection with Aspect Term Extraction and Aspect Term Polarity to further enhance performance. This approach facilitates shared representation learning, allowing Aspect Category Detection to benefit from the additional guidance offered by other tasks. Extensive experiments demonstrate that our framework surpasses strong baselines on standard datasets.