Abstract
The vector representations of words are very useful in different natural language processing tasks in order to capture the semantic meaning of words. In this context, the three known methods are: LSA, Word2Vec and GloVe. In this paper, these methods will be investigated in the field of topic segmentation for both languages Arabic and English. Moreover, Word2Vec is studied in depth by using different models and approximation algorithms. As results, we found out that LSA, Word2Vec and GloVe depend on the used language. However, Word2Vec presents the best word vector representation yet it depends on the choice of model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
