TAILIEUCHUNG - Báo cáo khoa học: "Lexical Normalisation of Short Text Messages: Makn Sens a #twitter"

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. . | Lexical Normalisation of Short Text Messages Makn Sens a twitter Bo Han and Timothy Baldwin NICTA Victoria Research Laboratory Department of Computer Science and Software Engineering The University of Melbourne hanb@ tb@ Abstract Twitter provides access to large volumes of data in real time but is notoriously noisy hampering its utility for NLP. In this paper we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn t require any annotations and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter. 1 Introduction Twitter and other micro-blogging services are highly attractive for information extraction and text mining purposes as they offer large volumes of real-time data with around 65 millions tweets posted on Twitter per day in June 2010 Twitter 2010 . The quality of messages varies significantly however ranging from high quality newswire-like text to meaningless strings. Typos ad hoc abbreviations phonetic substitutions ungrammatical structures and emoticons abound in short text messages causing grief for text processing tools Sproat et al. 2001 Ritter et al. 2010 . For instance presented with the input u must be talkin bout the paper but I was thinkin movies You must be talking about the paper but I was thinking movies 1 the Stanford parser Klein and 1 Throughout the paper we will provide a normalised version of examples as a gloss in double quotes. 368 Manning 2003 de Marneffe et al. 2006 analyses bout the paper and thinkin movies as a clause and noun phrase respectively rather than a prepositional phrase and verb phrase. If there were some way of .

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