# Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets

arXiv: https://arxiv.org/abs/1709.05094 · HTML: https://mlsanity.com/paper/1709.05094/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2017.

## Built on by (most cited first, 12 of 12)

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- [GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2104.06230/) (2021)
- [AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs](https://mlsanity.com/paper/2409.09704/) (2024)
- [Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization](https://mlsanity.com/paper/2305.13066/) (2023)
- [Toward Open-domain Slot Filling via Self-supervised Co-training](https://mlsanity.com/paper/2303.13801/) (2023)
- [A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning](https://mlsanity.com/paper/2403.17254/) (2024)
- [Distilling Task-specific Logical Rules from Large Pre-trained Models](https://mlsanity.com/paper/2210.02768/) (2022)
- [Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction](https://mlsanity.com/paper/2210.06829/) (2022)
- [Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation](https://mlsanity.com/paper/2508.19966/) (2025)

## Abstract (arXiv)

Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA) contest. The small amount of available datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsupervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. Moreover, it can be used efficiently as feature extractor and classifier for unsupervised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our automatically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80%. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.