30 research outputs found
Text Induced Spelling Correction
We present TISC, a language-independent and context-sensitive spelling checking and correction system designed to facilitate the automatic removal of non-word spelling errors in large corpora. Its lexicon is derived from a very large corpus of raw text, without supervision, and contains word unigrams and word bigrams. It is stored in a novel representation based on a purpose-built hashing function, which provides a fast and computationally tractable way of checking whether a particular word form likely constitutes a spelling error and of retrieving correction candidates. The system employs input context and lexicon evidence to automatically propose a limited number of ranked correction candidates when insufficient information for an unambiguous decision on a single correction is available. We describe the implemented prototype and evaluate it on English and Dutch text, containing real-world errors in more or less limited contexts. The results are compared with those of the isolated word spelling checking programs Ispell and the Microsoft Proofing Tools MPT
All, and only, the errors: More complete and consistent spelling and OCR-error correction evaluation
CLAM: Quickly deploy NLP command-line tools on the web
In this paper we present the software CLAM; the Computational Linguistics Application Mediator. CLAM is a tool that allows you to quickly and transparently transform command-line NLP tools into fully-fledged RESTful webservices with which automated clients can communicate, as well as a generic webapplication interface for human end-users