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.
A large portion of early work on negation detection @cite_15 @cite_47 @cite_43 has been done within the biomedical domain due to the availability of the BioScope corpus @cite_20 , which is annotated for negation cues and scopes. Interest in the task was further spurred by the *SEM shared task @cite_63 , which focused on detection of negation cues and scopes, in addition to detection of negated events and their so-called focus. The shared task made available the ConanDoyle-neg corpus, which is described in below. A number of systems were submitted for this task, employing a wide variety of strategies. For example, the best performing systems for the closed track and open track employed, respectively, SVM-based ranking of constituent (sub-)trees @cite_0 and CRF-based sequence-labeling using dependency features @cite_12 .
@cite_47 @cite_0 @cite_43 @cite_63 @cite_15 @cite_12 @cite_20
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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 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 that capture the scopal behavior of certain tokens. Following labeling, negated tokens are assigned to their respective cues using simple post-processing heuristics. The system was ranked first in the open track and third in the closed track, and was one of the top performers in the scope resolution sub-task overall. Finding negation signals and their scope in text is an important subtask in information extraction. In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system combines several classifiers and works in two phases. To investigate the robustness of the approach, the system is tested on the three subcorpora of the BioScope corpus representing different text types. It achieves the best results to date for this task, with an error reduction of 32.07 compared to current state of the art results. Objective: Negation is common in clinical documents and is an important source of poor precision in automated indexing systems. Previous research has shown that negated terms may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in clinical radiology reports. Design: Negations are classified based upon the syntactical categories of negation signals, and negation patterns, using regular expression matching. Negated terms are then located in parse trees using corresponding negation grammar. Measurements: A classification of negations and their corresponding syntactical and lexical patterns were developed through manual inspection of 30 radiology reports and validated on a set of 470 radiology reports. Another 120 radiology reports were randomly selected as the test set on which a modified Delphi design was used by four physicians to construct the gold standard. Results: In the test set of 120 reports, there were a total of 2,976 noun phrases, of which 287 were correctly identified as negated (true positives), along with 23 undetected true negations (false negatives) and 4 mistaken negations (false positives). The hybrid approach identified negated phrases with sensitivity of 92.6 (95 CI 90.9- 93.4 ), positive predictive value of 98.6 (95 CI 96.9-99.4 ), and specificity of 99.87 (95 CI 99.7-99.9 ). Conclusion: This novel hybrid approach can accurately locate negated concepts in clinical radiology reports not only when in close proximity to, but also at a distance from, negation signals.