This paper provides a comparative analysis of the performance of four state-of-the-art distributional semantic models DSMs over 11 languages, contrasting the native language-specific models with the use of machine translation over English-based DSMs. The experimental results show that there is a significant improvement average of 16.7i¾? for the Spearman correlation by using state-of-the-art machine translation approaches. The results also show that the benefit of using the most informative corpus outweighs the possible errors introduced by the machine translation. For all languages, the combination of machine translation over the Word2Vec English distributional model provided the best results consistently average Spearman correlation of0.68.
Navigli & Ponzetto @cite_9 propose an approach to compute semantic relatedness exploiting the joint contribution of different languages mediated by lexical and semantic knowledge bases. The proposed model uses a graph-based approach of joint multi-lingual disambiguated senses which outperforms the monolingual scenario and achieves competitive results for both resource-rich and resource-poor languages.
@cite_9
165057458
We present a knowledge-rich approach to computing semantic relatedness which exploits the joint contribution of different languages. Our approach is based on the lexicon and semantic knowledge of a wide-coverage multilingual knowledge base, which is used to compute semantic graphs in a variety of languages. Complementary information from these graphs is then combined to produce a 'core' graph where disambiguated translations are connected by means of strong semantic relations. We evaluate our approach on standard monolingual and bilingual datasets, and show that: i) we outperform a graph-based approach which does not use multilinguality in a joint way; ii) we achieve uniformly competitive results for both resource-rich and resource-poor languages.