1999/07/07 by David Elworthy, Elworthy, David · 1 citation
Computer Science · #Authorship Attribution and Profiling #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #I.5.3 #Natural Language Processing Techniques #Text and Document Classification Technologies #cs.CL
paper · pdf · doi:10.48550/arxiv.cs/9907010
8 pages; needs colacl.sty. Appeared in Proceedings of the Sixth Workshop on Very Large Corpora (COLING-ACL 98)
arxiv created 1999/07/07 · openalex publication_date 1999/07/07 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A statistical classification algorithm and its application to language identification from noisy input are described. The main innovation is to compute confidence limits on the classification, so that the algorithm terminates when enough evidence to make a clear decision has been made, and so avoiding problems with categories that have similar characteristics. A second application, to genre identification, is briefly examined. The results show that some of the problems of other language identification techniques can be avoided, and illustrate a more important point: that a statistical language process can be used to provide feedback about its own success rate.