# Language Independent Sequence Labelling for Opinion Target Extraction

arXiv: https://arxiv.org/abs/1901.09755 · HTML: https://mlsanity.com/paper/1901.09755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques](https://mlsanity.com/paper/2407.03748/) (2024)
- [Social Analysis of Young Basque Speaking Communities in Twitter](https://mlsanity.com/paper/2109.03487/) (2021)
- [Cross-lingual Argument Mining in the Medical Domain](https://mlsanity.com/paper/2301.10527/) (2023)

## Abstract (arXiv)

In this research note we present a language independent system to model Opinion Target Extraction (OTE) as a sequence labelling task. The system consists of a combination of clustering features implemented on top of a simple set of shallow local features. Experiments on the well known Aspect Based Sentiment Analysis (ABSA) benchmarks show that our approach is very competitive across languages, obtaining best results for six languages in seven different datasets. Furthermore, the results provide further insights into the behaviour of clustering features for sequence labelling tasks. The system and models generated in this work are available for public use and to facilitate reproducibility of results.