tRuEcasIng

Truecasing is the process of restoring case information to badly-cased or noncased text. This paper explores truecasing issues and proposes a statistical, language modeling based truecaser which achieves an accuracy of ∼98% on news articles. Task based evaluation shows a 26% F-measure improvement in named entity recognition when using truecasing. In the context of automatic content extraction, mention detection on automatic speech recognition text is also improved by a factor of 8. Truecasing also enhances machine translation output legibility and yields a BLEU score improvement of 80.2%. This paper argues for the use of truecasing as a valuable component in text processing applications.

• Type: Conference
• Year: 2003
• Where: ACL
• Authors: Lucian Vlad Lita, Abraham Ittycheriah, Salim Roukos, Nanda Kambhatla
• Keywords: ACL 2003, ACL 2007, BLEU Score Improvement, Named Entity Recognition, Speech Recognition Text