• Refine your search:
  • 
    Keyword
  • 
    Creator / Publisher
  • 
    Year:
    
    • 2010 (4)
  • 
    Medium:
    
    • Print (2)
    • Online (2)
  • 
    Type:
    
    • Article (2)
    • Miscellaneous (2)
  • 
    BLLDB-Access

Search in the Catalogues and Directories

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 Hits 1 – 4 of 4 1 Interlingual annotation of parallel text corpora: a new framework for annotation and evaluation Siddharthan, Advaith; Habash, Nizar; Passonneau, Rebecca J.... In: Natural language engineering. - Cambridge : Cambridge University Press 16 (2010) 3, 197-243 BLLDB OLC Linguistik Show details 2 Symbolic-to-statistical hybridization: extending generation-heavy machine translation Dorr, Bonnie Jean; Monz, Christof; Habash, Nizar In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 23 (2010) 1, 23-63 BLLDB OLC Linguistik Show details 3 Automatic Identification of Errors in Arabic Handwriting Recognition Roth, Ryan; Habash, Nizar Y.; Habash, Nizar. - : Center for Computational Learning Systems, Columbia University, 2010 BASE Show details 4 Automatic Identification of Errors in Arabic Handwriting Recognition ... Habash, Nizar; Roth, Ryan; Habash, Nizar Y.; Roth, Ryan M.. - : 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://academiccommons.columbia.edu/doi/10.7916/D8SQ95Q9 https://dx.doi.org/10.7916/d8sq95q9 BASE Hide details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 2 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 2 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 2 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0