# Language Independent Sequence Labelling for Opinion Target Extraction

## TL;DR

This paper introduces a language-independent sequence labelling system for Opinion Target Extraction that performs competitively across multiple languages and datasets, enhancing the robustness of aspect-based sentiment analysis.

## Contribution

It presents a novel language-independent approach using clustering features for opinion target extraction as sequence labelling, with extensive multilingual evaluation.

## Key findings

- Achieved top results in six languages across seven datasets.
- Demonstrated the effectiveness of clustering features in sequence labelling.
- Provided publicly available models for reproducibility.

## Abstract

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.