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Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with(More)
This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple(More)
This paper describes the second of two systems submitted from the University of Oslo (UiO) to the 2012 *SEM Shared Task on resolving negation. The system combines SVM cue classification with CRF sequence labeling of events and scopes. Models for scopes and events are created using lexical and syntactic features, together with a fine-grained set of labels(More)
In this work, we revisit Shared Task 1 from the 2012 *SEM Conference: the automated analysis of negation. Unlike the vast majority of participating systems in 2012, our approach works over explicit and formal representations of proposi-tional semantics, i.e. derives the notion of negation scope assumed in this task from the structure of logical-form meaning(More)
Introduction In Short good results with classical pipeline explicit connectives and arguments: adapted approach from detection of speculation and negation (Velldal et al. 2012, Read et al. 2012) cross-validation on training set sense disambiguation: ensemble classifier
many open source natural language processing technologies and advancements. ("This is a sentence/ncut off in the middle because pdf. If you want to get Computer Science Handbook, Second Edition pdf eBook copy write by good Handbook of Natural Language Processing (second edition). Information technology involving natural language, to improve productivity(More)
Proper treatment of negation is an important characteristic of methods for sentiment analysis. However, while there is a growing body of research on the automatic resolution of negation, it is not yet clear as to how negation is best represented for different applications. To begin to address this issue, we review representation alternatives and present a(More)
This paper describes the first of two systems submitted from the University of Oslo (UiO) to the 2012 *SEM Shared Task on resolving negation. Our submission is an adaption of the negation system of Velldal et al. (2012), which combines SVM cue classification with SVM-based ranking of syntactic constituents for scope resolution. The approach further extends(More)
Segmenting documents into discrete, sentence-like units is usually a first step in any natural language processing pipeline. However, current segmentation tools perform poorly on text that contains markup. While stripping markup is a simple solution, we argue for the utility of the extra-linguistic information encoded by markup and present a scheme for(More)
An important sub-task of sentiment analysis is polarity classification, in which text is classified as being positive or negative. Supervised machine learning techniques can perform this task very effectively. However, they require a large corpus of training data, and a number of studies have demonstrated that the good performance of supervised models is(More)