Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Lexical semantic techniques for corpus analysis
Computational Linguistics - Special issue on using large corpora: II
Revisiting the readability assessment of texts in Portuguese
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
"I don't know where he is not": does deception research yet offer a basis for deception detectives?
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
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In this paper, we present a new approach to writing tools that extends beyond the rudimentary spelling and grammar checking to the content of the writing itself. Linguistic methods have long been used to detect familiar lexical patterns in the text to aid automatic summarization and translation of documents. We apply these methods to determine the quality of the text and implement new techniques for measuring readability and providing feedback to authors on how to improve the quality of their documents. We take an extended view of readability that considers text cohesion, propositional density, and word familiarity. We provide simple feedback to the user detailing the most and least readable sentences, the sentences most densely packed with information and the most cohesive words in their document. Commonly used verbose words and phrases in the text, as identified by The Plain English Campaign, can be replaced with user-selected replacements. Our techniques were implemented as a free download extension to the Open Office word processor generating 6,500 downloads to date.