Abstract:The increasing impact of Web 2.0 involves a growing usage of slang, abbreviations, and emphasized words, which limit the performance of traditional natural language processing models. The state-of-the-art Part-of-Speech (POS) taggers are often unable to assign a meaningful POS tag to all the words in a Web 2.0 text.To solve this limitation, we are proposing an auxiliary POS tagger that assigns the POS tag to a given token based on the information deriving from a sequence of preceding and following POS tags. Th… Show more
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