Summary of the paper
| Title | Arabic Part of Speech Tagging |
|---|---|
| Authors | Emad Mohamed and Sandra Kübler |
| Abstract | Arabic is a morphologically rich language, which presents a challenge for partof speech tagging. In this paper, we compare two novel methods for POS taggingof Arabic without the use of gold standard word segmentation but with the fullPOS tagset of the Penn Arabic Treebank. The first approach uses complex tagsthat describe full words and does not require any word segmentation. The secondapproach is segmentation-based, using a machine learning segmenter. In thisapproach, the words are first segmented, then the segments are annotated withPOS tags. Because of the word-based approach, we evaluate full word accuracyrather than segment accuracy. Word-based POS tagging yields better results thansegment-based tagging (93.93% vs. 93.41%). Word based tagging also gives thebest results on known words, the segmentation-based approach gives betterresults on unknown words. Combining both methods results in a word accuracy of94.37%, which is very close to the result obtained by using gold standardsegmentation (94.91%). |
| Language | Other |
| Topics | Part of speech tagging, Corpus (creation, annotation, etc.), Other |
Full paper  |
Arabic Part of Speech Tagging |
| Bibtex | @InProceedings{MOHAMED10.384, author = {Emad Mohamed and Sandra Kübler}, title = {Arabic Part of Speech Tagging}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |