A study of conceptual language similarity: comparison and evaluation
This work addresses linguistic diversity in NLP, particularly for low-resource languages, but is incremental as it builds on prior methods.
The paper tackled the problem of measuring language similarity by introducing a conceptual approach based on how languages represent basic concepts, and evaluated it on a binary classification task, showing it complements existing measures.
An interesting line of research in natural language processing (NLP) aims to incorporate linguistic typology to bridge linguistic diversity and assist the research of low-resource languages. While most works construct linguistic similarity measures based on lexical or typological features, such as word order and verbal inflection, recent work has introduced a novel approach to defining language similarity based on how they represent basic concepts, which is complementary to existing similarity measures. In this work, we study the conceptual similarity in detail and evaluate it extensively on a binary classification task.