Journal
EGYPTIAN INFORMATICS JOURNAL
Volume 22, Issue 1, Pages 35-43Publisher
CAIRO UNIV, FAC COMPUTERS & INFORMATION
DOI: 10.1016/j.eij.2020.03.002
Keywords
Word embeddings; Deep learning; Arabic morphology; Galois power of two; Real-time embeddings; Parallel neural model graph
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Funding
- EIAS Data Science & Blockchain Lab, Prince Sultan University
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GPOW2 is a real-time embedding scheme that computes multilevel embeddings on the fly, improving the performance and accuracy of the SWAM Arabic morphological engine.
This paper describes how a simple novel Galois Power-of-Two (GPOW2) real-time embedding scheme is used to improve the performance and accuracy of downstream NLP tasks. GPOW2 computes embeddings live on the fly (real time) in the context of target NLP tasks without the need for tabulated pre-embeddings. One excellent feature of the method is the ability to capture multilevel embeddings in the same pass. It simultaneously computes character, word and sentence embeddings on the fly. GPOW2 has been derived in the context of attempts to improve the performance of the SWAM Arabic morphological engine, which is a multipurpose tool that supports segmentation, classification, POS tagging, spell checking, word embeddings, sematic search, among other tasks. SWAM is a pattern-oriented algorithm that relies on morphological patterns and POS tagging to perform NLP tasks. The paper demonstrates how GPOW2 led to improvements in the accuracy of POS tagging and pattern matching, and accordingly the performance of the whole engine. The accuracy for pattern prediction is 99.47% and is 98.80% for POS tagging. (C) 2020 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.
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