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sem 2013 shared task: Semantic textual similarity, including a pilot on typed-similarity
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In: http://www.aclweb.org/anthology/S/S13/S13-1004.pdf (2013)
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Coleur and colslm: A wsd approach to multilingual lexical substitution, tasks 2 and 3 semeval 2010
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In: http://aclweb.org/anthology/S/S10/S10-1026.pdf (2010)
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Combining orthogonal monolingual and multilingual sources of evidence for all words WSD
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In: http://www.cs.columbia.edu/nlp/papers/2010/P10-1156.pdf (2010)
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Arabic named entity recognition using optimized feature sets
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In: http://www.aclweb.org/anthology-new/D/D08/D08-1030.pdf (2008)
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Specific Features to Enhance Arabic Named Entity Recognition
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Semantic Role Labeling Systems for Arabic using Kernel Methods."Proceedings of ACL-08: HLT
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In: http://disi.unitn.it/moschitti/articles/ACL2008.pdf (2008)
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A pilot Arabic PropBank
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In: http://papers.ldc.upenn.edu/LREC2008/Pilot_Arabic_Propbank.pdf (2008)
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workshop on Graph-based Algorithms for Natural Language Processing Workshop chairs:
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In: http://www.aclweb.org/anthology-new/W/W08/W08-20.pdf (2008)
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Semi-Automatic Error Analysis for Large-Scale Statistical Machine Translation Systems
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In: http://www.mt-archive.info/MTS-2007-Kirchhoff.pdf (2007)
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Developing and Using a Pilot Dialectal Arabic Treebank
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In: http://papers.ldc.upenn.edu/LREC2006/PilotDialectalArabicTreebank.pdf (2006)
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Parsing arabic dialects
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In: http://acl.ldc.upenn.edu/eacl2006/main/papers/26_1_chiangetal_218.pdf (2005)
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Parsing arabic dialects
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In: http://www.umiacs.umd.edu/~dchiang/papers/CDHRS-eacl06.pdf (2005)
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Automatic tagging of Arabic text: from raw text to base phrase chunks
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In: http://www.stanford.edu/~jurafsky/ArabicChunk.pdf (2004)
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Abstract:
To date, there are no fully automated systems addressing the community’s need for fundamental language processing tools for Arabic text. In this paper, we present a Support Vector Machine (SVM) based approach to automatically tokenize (segmenting off clitics), part-ofspeech (POS) tag and annotate base phrases (BPs) in Arabic text. We adapt highly accurate tools that have been developed for English text and apply them to Arabic text. Using standard evaluation metrics, we report that the SVM-TOK tokenizer achieves an ¡£¢¥¤£ ¦ score of 99.12, theSVM-POS tagger achieves an accuracy of 95.49%, and the SVM-BP chunker yields an ¡ ¢§¤£ ¦ score of 92.08. 1
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URL: http://www.stanford.edu/~jurafsky/ArabicChunk.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.2147
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An Unsupervised Method for Word Sense Tagging using Parallel Corpora
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In: http://acl.ldc.upenn.edu/P/P02/P02-1033.pdf (2002)
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An Unsupervised Method for Word Sense Tagging using Parallel Corpora
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In: http://www.umiacs.umd.edu/~resnik/pubs/acl02mona.ps (2002)
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A Statistical Word-Level Translation Model for Comparable Corpora
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In: ftp://ftp.cs.umd.edu/pub/papers/papers/ncstrl.umcp/CS-TR-4150/CS-TR-4150.ps.Z (2000)
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Measuring Verb Similarity
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In: http://lamp.cfar.umd.edu/Media/Publications/Papers/LAMP_047/LAMP_047.ps (2000)
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Measuring Verb Similarity
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In: http://www.ircs.upenn.edu/cogsci2000/PRCDNGS/SPRCDNGS/PAPERS/RES-DIA.PDF (2000)
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A Statistical Word-Level Translation Model for Comparable Corpora
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In: http://www.umiacs.umd.edu/lamp/pubs/TechReports/LAMP_048/LAMP_048.pdf (2000)
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A Statistical Word-Level Translation Model for Comparable Corpora
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In: http://133.23.229.11/~ysuzuki/Proceedingsall/RIAO2000/Friday/124DP2.ps (2000)
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