Comparison between Two Arabic Tagsets
Enhancing Arabic tagging is of great importance in many NLP applications. This paper presents a simple comparison tool that compares two powerful tagging systems for Arabic, the first one is the ASVM Tagger, by Diab M. et al,. The second one is RDI Arab Tagger that relies on simple powerful long n-grams probability estimation plus A*search algorithm for disambiguation, this comparison is done to superimpose points of excellence in Arab Tagger into ASVM tagger. From this comparison, mapper tool is implemented to convert from the fine grain Arab tagset (62 tags used by the ArabTagger) to the other course grain compact tagset of 24 tags Reduced Tagset (RTS) used by ASVM-Tagger. A combined system from the output of both is then formed, which gives an average accuracy higher than that of ASVM in our experiment, 95% of hybrid system versus 93% of ASVM system.
Part-of-Speech Tagging (POS) Automatic Support Vector Machine (ASVM) Reduced Tag Set (RTS) N-gram model A*search algorithm
Mohsen A. A. RASHWAN Enas A. H. KHALIL Ahmed RAFEA
Department of Electronics & Electrical Communications, Faculty of Eng, Cairo Univ/ the Engineering C Department of Systems&Information Electrical/Engineering Division, National Research Centre (NRC), E Department of Computer Science, American University in Cairo (AUC) Cairo, Egypt
国际会议
大连
英文
1-8
2009-09-24（万方平台首次上网日期，不代表论文的发表时间）