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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 37 1 Conventional Orthography for Dialectal Arabic (CODA): Principles and Guidelines -- Egyptian Arabic - Version 0.7 - March 2012 Habash, Nizar Y.; Diab, Mona T.; Rambow, Owen C.. - : Center for Computational Learning Systems, Columbia University, 2014 BASE Show details 2 Conventional Orthography for Dialectal Arabic (CODA): Principles and Guidelines -- Egyptian Arabic - Version 0.7 - March 2012 ... Habash, Nizar Y.; Diab, Mona T.; Rambow, Owen C.. - : Columbia University, 2014 BASE Show details 3 Annotation Guidelines for Arabic Nominal Gender, Number, and Rationality Habash, Nizar Y.; Alkuhlani, Sarah M.. - : Center for Computational Learning Systems, Columbia University, 2013 BASE Show details 4 LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual Diab, Mona T.; Habash, Nizar Y.; Rambow, Owen C.. - : Center for Computational Learning Systems, Columbia University, 2013 BASE Show details 5 Annotation Guidelines for Arabic Nominal Gender, Number, and Rationality ... Habash, Nizar Y.; Alkuhlani, Sarah M.. - : Columbia University, 2013 BASE Show details 6 LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual ... Diab, Mona T.; Habash, Nizar Y.; Rambow, Owen C.. - : Columbia University, 2013 BASE Show details 7 Improved arabic-to-english statistical machine translation by reordering post-verbal subjects for word alignment Carpuat, Marine; Marton, Yuval; Habash, Nizar Y.. - 2012 BASE Show details 8 A Modern Standard Arabic Closed-Class Word List Salloum, Wael Sameer; Habash, Nizar Y.. - : Center for Computational Learning Systems, Columbia University, 2012 BASE Show details 9 Can Automatic Post-Editing Make MT More Meaningful? McKeown, Kathleen; Parton, Kristen; Habash, Nizar Y.. - 2012 BASE Show details 10 A Modern Standard Arabic Closed-Class Word List ... Salloum, Wael Sameer; Habash, Nizar Y.. - : Columbia University, 2012 BASE Show details 11 Can Automatic Post-Editing Make MT More Meaningful? ... McKeown, Kathleen; Parton, Kristen; Habash, Nizar Y.. - : Columbia University, 2012 BASE Show details 12 Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation Habash, Nizar Y.; Salloum, Wael Sameer. - : Center for Computational Learning Systems, Columbia University, 2011 BASE Show details 13 Conventional Orthography for Dialectal Arabic (CODA) Version 0.1 ““ July 2011 Habash, Nizar Y.; Diab, Mona T.; Rambow, Owen C.. - : Center for Computational Learning Systems, Columbia University, 2011 BASE Show details 14 Conventional Orthography for Dialectal Arabic (CODA) Version 0.1 ““ July 2011 ... Habash, Nizar Y.; Diab, Mona T.; Rambow, Owen C.. - : Columbia University, 2011 BASE Show details 15 Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation ... Salloum, Wael Sameer; Habash, Nizar Y.. - : Columbia University, 2011 BASE Show details 16 Automatic Identification of Errors in Arabic Handwriting Recognition Roth, Ryan; Habash, Nizar Y.; Habash, Nizar; Roth, Ryan M.. - : Center for Computational Learning Systems, Columbia University, 2010 Abstract: Arabic handwriting recognition (HR) is a challenging problem due to Arabic's connected letter forms, consonantal diacritics and rich morphology. In this paper we isolate the task of identification of erroneous words in HR from the task of producing corrections for these words. We consider a variety of linguistic (morphological and syntactic) and non-linguistic features to automatically identify these errors. We also consider a learning curve varying in two dimensions: number of segments and number of n-best hypotheses to train on. We additionally evaluate the performance on different test sets with different degrees of errors in them. Our best approach achieves a roughly ~20% absolute increase in F-score over a simple but reasonable baseline. A detailed error analysis shows that linguistic features, such as lemma models, help improve HR-error detection precisely where we expect them to: semantically inconsistent error words. Keyword: Artificial intelligence; Computer science URL: https://doi.org/10.7916/D8SQ95Q9 BASE Hide details 17 Automatic Identification of Errors in Arabic Handwriting Recognition ... Habash, Nizar; Roth, Ryan; Habash, Nizar Y.. - : Columbia University, 2010 BASE Show details 18 Spoken Arabic Dialect Identification Using Phonotactic Modeling Biadsy, Fadi; Hirschberg, Julia Bell; Habash, Nizar Y.. - 2009 BASE Show details 19 Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-Based Pronunciation Rules Biadsy, Fadi; Hirschberg, Julia Bell; Habash, Nizar Y.. - 2009 BASE Show details 20 CATiB: The Columbia Arabic Treebank Habash, Nizar Y.; Roth, Ryan; Habash, Nizar. - : Center for Computational Learning Systems, Columbia University, 2009 BASE Show details Page: 1 2

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