An Evaluation of Sindhi Word Embedding in Semantic Analogies and Downstream Tasks

• URL: http://arxiv.org/abs/2408.15720v1
• Date: Wed, 28 Aug 2024 11:36:29 GMT
• Title: An Evaluation of Sindhi Word Embedding in Semantic Analogies and Downstream Tasks
• Authors: Wazir Ali, Saifullah Tumrani, Jay Kumar, Tariq Rahim Soomro,
• Abstract summary: 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. The cleaned vocabulary is fed to state-of-the-art continuous-bag-of-words, skip-gram, and GloVe word embedding algorithms.
• Score: 2.3624125155742064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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