---
title: "Position-Aware Tagging for Aspect Sentiment — Xu 2020"
description: "This paper addresses Aspect Sentiment Triplet Extraction (ASTE)."
authors: ["Lu Xu", "Hao Li", "Wei Lu", "Lidong Bing"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.183/"
doi: "10.18653/v1/2020.emnlp-main.183"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/"
---
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# Position-Aware Tagging for Aspect Sentiment Triplet Extraction

**Authors:** [Lu Xu](https://md.research.elamur.ai/free/en/authors/lu-xu/), Hao Li, [Wei Lu](https://md.research.elamur.ai/free/en/authors/wei-lu/), [Lidong Bing](https://md.research.elamur.ai/free/en/authors/lidong-bing/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.183/) · [PDF](https://aclanthology.org/2020.emnlp-main.183.pdf) · [DOI 10.18653/v1/2020.emnlp-main.183](https://doi.org/10.18653/v1/2020.emnlp-main.183) · [OpenAlex](https://openalex.org/W3105083537)

**Citations:** 273 citations (openalex-snapshot · 2026-10-14) · [97](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
This paper addresses Aspect Sentiment Triplet Extraction (ASTE). The task is to find each target in a sentence, its sentiment polarity, and the opinion span that explains that sentiment. The only earlier method (Peng et al., 2019) used two stages: it first labelled targets with sentiment and opinion spans, then paired them with an MLP classifier. The authors argue that the three elements depend strongly on one another and should be predicted together.

To do this, they extend the BIOES scheme. The tag on a target's first word (B or S) also records the polarity and two offsets that locate the edges of the linked opinion span. They prove this gives a one-to-one mapping to triplet sets when targets do not overlap and each target has one opinion span. The scheme can also represent overlapping opinion spans.

On top of this scheme they build JET, an end-to-end model that combines CRF and Semi-Markov CRF. It uses BiLSTM-based factorized scores for targets, sentiment, opinion spans and offsets. On four benchmark datasets, JET clearly outperforms existing baselines. This shows that a single tagging pass can replace pipeline extraction for this task.

## Key points
- **Tagging scheme:** new tags B^ε_{j,k} and S^ε_{j,k}, where ε ∈ {+, 0, −} and j, k are offsets to the opinion span's ends. For example, "food" gets S^0_{2,3}, pointing to "so so". A constraint M limits the maximum absolute offset.
- **Model:** the feature score combines separate components for target context (f_t), sentiment (f_s), opinion span (f_o) and offset (f_r). There are two complementary variants, JET_t and JET_o.
- **Evaluation:** experiments use 14Rest, 14Lap, 15Rest and 16Rest. With BERT and M = 6, JET_o reaches F1 of 62.40, 51.04, 57.53 and 63.83 respectively.
- **Ensemble:** the JET_t→o ensemble raises F1 to 62.51 (14Rest), 51.33 (14Lap) and 58.32 (15Rest). It mainly improves recall and is less effective on 16Rest.
- **Ablations and code:** removing offset features or opinion features lowers F1, while character embeddings add little. Code is available at github.com/xuuuluuu/Position-Aware-Tagging-for-ASTE.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

97 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 91 not yet.

- [Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions](https://md.research.elamur.ai/free/en/papers/cai-2021-aspect-category-opinion-sentiment-quadruple/)
- [Is GPT-3 a Good Data Annotator?](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/)
- [Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/smid-2026-extending-czech-aspect-based-sentiment/)
- [A Unified Generative Framework for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-aspect-based/)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/zhang-2021-aspect-sentiment-quad-prediction-as/)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2020position,
  title = {{Position-Aware Tagging for Aspect Sentiment Triplet Extraction}},
  author = {Xu, Lu and Li, Hao and Lu, Wei and Bing, Lidong},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.183},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.183}
}
```

- [← previous Politeness Transfer: A Tag and Generate Approach](https://md.research.elamur.ai/free/en/papers/madaan-2020-politeness-transfer-tag-generate-approach/)
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