Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in a data-driven manner. We describe our approach, a cascading neural architecture with selective sharing of LSTM layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.
While syntactic information as often been found useful for scope resolution, the task of cue detection appears to only require simpler surface information. @cite_43 present an approach to cue detection which treats the set of cue words as a closed class and apply a disambiguation-based approach to the problem of cue detection, showing that simple lexical features based on a narrow context window is sufficient to achieve good performance.
@cite_43
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Most previous studies in computerized deception detection have relied only on shallow lexico-syntactic patterns. This paper investigates syntactic stylometry for deception detection, adding a somewhat unconventional angle to prior literature. Over four different datasets spanning from the product review to the essay domain, we demonstrate that features driven from Context Free Grammar (CFG) parse trees consistently improve the detection performance over several baselines that are based only on shallow lexico-syntactic features. Our results improve the best published result on the hotel review data (, 2011) reaching 91.2 accuracy with 14 error reduction. This paper reports on a simple system for resolving the scope of negation in the closed track of the *SEM 2012 Shared Task. Cue detection is performed using regular expression rules extracted from the training data. Both scope tokens and negated event tokens are resolved using a Conditional Random Field (CRF) sequence tagger -- namely the SimpleTagger library in the MALLET machine learning toolkit. The full negation F1 score obtained for the task evaluation is 48.09 (P=74.02 , R=35.61 ) which ranks this system fourth among the six submitted for the closed track. Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these “dots” over time. An obvious short-coming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, real-world videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions. We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue compares facial features of the most frontal face detections in pairs of tracklets. The classifier cue learns a discriminative appearance model for each tracklet, using detection pairs within reliable tracklets and between overlapping tracklets as training data. The constraint cue observes the compatibility of motion of two tracklets. The association of tracklets is globally optimized with the Hungarian algorithm. We validate our approach on two challenging episodes of two TV series and report a Multiple Object Tracking Accuracy (MOTA) of 82 and 68.2 , respectively.