Web text has been successfully used as training data for many NLP applications. While most previous work accesses web text through search engine hit counts, we created a Web Corpus by downloading web pages to create a topic-diverse collection of 10 billion words of English. We show that for context-sensitive spelling correction the Web Corpus results are better than using a search engine. For thesaurus extraction, it achieved similar overall results to a corpus of newspaper text. With many more words available on the web, better results can be obtained by collecting much larger web corpora.
REF created a Web corpus for English that contained 10 billion words and showed that for content-sensitive spelling correction the Web corpus results were better than using a search engine.
Web Text Corpus For Natural Language Processing
Conference Of The European Association For Computational Linguistics
Computer Science