IRIT at INEX 2013 : Tweet Contextualization Track

Abstract

The paper presents IRIT's approach used at INEX Tweet Contextualization Track 2013. Systems had to provide a context to a tweet. This year we further modified our approach presented at INEX 2011 and 2012 underlain by the product of scores based on hashtag processing, TF-IDF cosine similarity measure enriched by smoothing from local context and document beginning, named entity recognition and part-of-speech weighting. We assumed that relevant sentences come from relevant documents therefore we multiply sentence score by document relevance. We also used generalized POS (e.g. we merge regular adverbs, superlative and comparative into a single adverb group). We introduced sentence quality measure based on Flesch reading ease test, lexical diversity, meaningful word ratio and punctuation ratio. Our approach was ranked first, second and third over 24 runs submitted by all participants on different reference pools according to informativeness evaluation. At the same time it obtained the best readability score.