An Evaluation of Sindhi Word Embedding in Semantic Analogies and Downstream Tasks
<|reference_start|>An Evaluation of Sindhi Word Embedding in Semantic Analogies and Downstream Tasks: In this paper, we propose a new word embedding based corpus consisting of more than 61 million words crawled from multiple web resources. We design a preprocessing pipeline for the filtration of unwanted text from crawled data. Afterwards, the cleaned vocabulary is fed to state-of-the-art continuous-bag-of-words, skip-gram, and GloVe word embedding algorithms. For the evaluation of pretrained embeddings, we use popular intrinsic and extrinsic evaluation approaches. The evaluation results reveal that continuous-bag-of-words and skip-gram perform better than GloVe and existing Sindhi fastText word embedding on both intrinsic and extrinsic evaluation approaches<|reference_end|>
@article{ali2024an,
    title={An Evaluation of Sindhi Word Embedding in Semantic Analogies and
  Downstream Tasks},
    author={Wazir Ali and Saifullah Tumrani and Jay Kumar and Tariq Rahim Soomro},
    journal={arXiv preprint arXiv:2408.15720},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15720},
    primaryClass={cs.CL}
}