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.
Traditional approaches to the task of negation detection have typically employed a wide range of hand-crafted features describing a number of both lexical, morphosyntactic and even semantic properties of the input text. Syntactic parsing has often been used to analyze the input prior to negation detection and has been based on both constituency-based representations @cite_0 @cite_22 , as well as dependency-based representations @cite_19 @cite_48 @cite_9 . The system of @cite_22 go even further and combines an existing system @cite_0 with an additional layer of manually defined rules over Minimal Recursion Semantics structures created by an HPSG parser.
@cite_22 @cite_48 @cite_9 @cite_0 @cite_19
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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 propositional semantics, i.e. derives the notion of negation scope assumed in this task from the structure of logical-form meaning representations. We relate the task-specific interpretation of (negation) scope to the concept of (quantifier and operator) scope in mainstream underspecified semantics. With reference to an explicit encoding of semantic predicate-argument structure, we can operationalize the annotation decisions made for the 2012 *SEM task, and demonstrate how a comparatively simple system for negation scope resolution can be built from an off-the-shelf deep parsing system. In a system combination setting, our approach improves over the best published results on this task to date. 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. 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 (2012), which combines SVM cue classification with SVM-based ranking of syntactic constituents for scope resolution. The approach further extends our prior work in that we also identify factual negated events. While submitted for the closed track, the system was the top performer in the shared task overall. 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 state-of-the-art system for negation resolution that is interoperable across these schemes. By employing different configurations of this system as a component in a test bed for lexically-based sentiment classification, we demonstrate that the choice of representation can have a significant impact on downstream processing.