Context-aware Models for Twitter Sentiment Analysis

Recent works on Sentiment Analysis over Twitter are tied to the idea that the sentiment can be completely captured after reading an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of this contextual information is investigated for the detection of the polarity of tweet messages. We modeled the polarity detection problem as a sequential classification task over streams of tweets. A Markovian formulation of the Support Vector Machine discriminative model has been here adopted to assign the sentiment polarity to entire sequences. The experimental evaluation proves that sequential tagging better embodies evidence about the contexts and is able to increase the accuracy of the resulting polarity detection process. These evidences are strengthened as experiments are successfully carried out over two different languages: Italian and English. Results are particularly interesting as the approach is flexible and does not rely on any manually coded resources

Efficient Parsing for Information Extraction

Several (and successfull) Information Extraction systems have recently replaced the core parsing components with shallow but more efficient recognizers. In this paper we argue that the absence of an underlying grammatical recognizer, given the complex nature of several (non-english) languages, is a strong limitation for text processing functionalities, like those an IE system needs. We propose a robust and efficient syntactic recognizer mainly aimed to capture grammatical information crucial for several linguistic and non linguistic inferences. The proposed system is based on a novel architecture exploiting two major principles: lexicalization and stratification of the parsing process. As several linguistic theories (e.g. HPSG) and parsing frameworks (e.g. LTAG, SLTAG, lexicalized probabilistic parsing) suggest, lexicon-driven systems ensure the suitable forms of grammatical control for many complex phenomena. In our system an analysis guided by information on typical verb projections (e.g. verb subcategorization structures) is coupled with extended locality constraints (i.e. recognition of clause boundaries). Furthermore, stratification is also employed. A cascade of processing steps starts from chunk recognition and proceeds through clause analysis to dependency detection. Recognition of chunks allows to minimize the input ambiguity to the remaining phases. The resulting system is thus robust against ungrammatical phenomena (e.g. complex clause embedding, misspellings, unknown words). Efficiency is also retained, although ambiguous phenomena (multiple PP attachments) are recognized

Efficient Parsing for Information Extraction

1 Introduction Several (and successful) IE systems have recently replaced the core parsing components with shallow but more efficient recognizers [1, 8]. However, the absence of a grammatical rec- ognizer, given the complex nature of several (non-english) lan- guages, is a strong limitation for text processing functionali- ties, like those an IE system needs. Let us provide a sentence, extracted and translated from a financial corpus in Italian: