Name
Abstract | ||
|---|---|---|
The objective of this work is to develop a POS tagger for the Arabic language. This analyzer uses a very rich tag set that gives syntactic information about proclitic attached to words. This study employs a probabilistic model and a morphological analyzer to identify the right tag in the context. Most published research on probabilistic analysis uses only a training corpus to search the probable tags for each words, and this sometimes affects their performances. In this paper, we propose a method that takes into account the tags that are not included in the training data. These tags are proposed by the Alkhalil_Morpho_Sys analyzer (Bebah et al. 2011). We show that this consideration increases significantly the accuracy of the morphosyntactic analysis. In addition, the adopted tag set is very rich and it contains the compound tags that allow analyze the proclitics attached to words. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10772-015-9302-8 | I. J. Speech Technology |
Keywords | Field | DocType |
Part of speech tagging, Morphological analysis, Hidden Markov model, Smoothing, Training set, Testing set | Training set,Arabic,Computer science,Probabilistic analysis of algorithms,Speech recognition,Morpho,Smoothing,Artificial intelligence,Natural language processing,Statistical model,Hidden Markov model,Syntax | Journal |
Volume | Issue | ISSN |
19 | 2 | 1572-8110 |
Citations | PageRank | References |
6 | 0.64 | 10 |
Authors | ||
2 | ||