In this paper 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, at the time of writing, best results for six languages in seven different datasets. Furthermore, the results provide further insights into the behaviour of clustering features for sequence labeling tasks. Finally, we also show that these results can be outperformed by recent advances in contextual word embeddings and the transformer architecture. The system and models generated in this work are available for public use and to facilitate reproducibility of results.