A New Corpus for Low-Resourced Sindhi Language with Word Embeddings
Table 9: Comparison of semantic and syntactic accuracy of proposed word embeddings using WordSim-353 dataset on 300−D embedding choosing various window size (ws).
['Model', '[ITALIC] ws', 'Accuracy']
We evaluate the performance of our proposed word embeddings using the WordSim353 dataset by translation English word pairs to Sindhi. Due to vocabulary differences between English and Sindhi, we were unable to find the authentic meaning of six terms, so we left these terms untranslated. So our final Sindhi WordSim353 consists of 347 word pairs. The SG model outperforms CBoW and GloVe in semantic and syntactic similarity by achieving the performance of 0.629 with ws=7. Therefore, despite the challenges in translation from English to Sindhi, our proposed Sindhi word embeddings have efficiently captured the semantic and syntactic relationship.