About this document
Universal Word Segmentation Framework by RaziAhmed is a document available to read on EtoBox.
This document presents a universal word segmentation model that can segment words across many languages with diverse writing systems. The model uses a bidirectional recurrent neural network with conditional random fields (BiRNN-CRF) to perform sequence tagging. It analyzes how various typological factors like character set size, lexicon size, average word length, and segmentation frequency affect segmentation accuracy across languages. Experimental results show the model achieves state-of-the-art accuracy o
- Author
- RaziAhmed
- Language
- EN