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2022 SemEval SemEval 2022

Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity Classifier

Abstract

AbstractWe present our contribution to the SemEval 22 Share Task 8: Multilingual news article similarity. The approach is lightweight and language-agnostic, it is based on the computation of several lexicographic and embedding-based features, and the use of a simple ML approach: random forests. In a notable departure from the task formulation, which is a ranking task, we tackled this task as a classification one. We present a detailed analysis of the behaviour of our system under different settings.

Authors

Nicolas Stefanovitch

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Regression Machine Learning > Core Methods > Embedding Learning Machine Learning > Application Areas > Domain Adaptation

Keywords

feature engineering random forest text similarity multilingual similarity embedding-based feature lexicographic feature
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