Publication Type : Journal Article
Publisher : Proceedings of SemEval
Source : Proceedings of SemEval, p.1022–1027 (2016)
Url : https://pdfs.semanticscholar.org/31c5/905e6eeb0c4e5a78646fb746a3c42fb4ec5b.pdf
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN), Computer Science
Year : 2016
Abstract : Complex word identification task focuses on identifying the difficult word from English sentence for a Non-Native speakers. NonNative speakers are those who don’t have English as their native language. It is a subtask for lexical simplification. We have experimented with word embedding features, orthographic word features, similarity features and POS tag features which improves the performance of the classification. In addition to the SemEval 2016 results we have evaluated the training data by varying the vector dimension size and obtained the best possible size for producing better performance. The SVM learning algorithm will attains constant and maximum accuracy through linear classifier. We achieve a G-score of 0.43 and 0.54 on computing complex words for two systems.
Cite this Research Publication : S. P. Sanjay, Dr. M. Anand Kumar, and Dr. Soman K. P., “AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding”, Proceedings of SemEval, pp. 1022–1027, 2016.