35,621 research outputs found
Ensemble Deep Learning for Aspect-based Sentiment Analysis
Sentiment analysis is a subfield of Natural Language Processing (NLP) which tries to process a text to extract opinions or attitudes towards topics or entities. Recently, the use of deep learning methods for sentiment analysis has received noticeable attention from researchers. Generally, different deep learning methods have shown superb performance in sentiment analysis problem. However, deep learning models are different in nature and have different strengths and limitations. For example, convolutional neural networks are useful for extracting local structures from data, while recurrent models are able to learn order dependence in sequential data. In order to combine the advantages of different deep models, in this paper we have proposed a novel approach for aspect-based sentiment analysis which utilizes deep ensemble learning. In the proposed method, we first build four deep learning models, namely CNN, LSTM, BiLSTM and GRU. Then the outputs of these models are combined using stacking ensemble approach where we have used logistic regression as meta-learner. The results of applying the proposed method on the real datasets show that our method has increased the accuracy of aspect-based prediction by 5% to 20% compared to the basic deep learning methods
Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning
Deep learning techniques have dominated the literature on aspect-based
sentiment analysis (ABSA), yielding state-of-the-art results. However, these
deep models generally suffer from spurious correlation problems between input
features and output labels, which creates significant barriers to robustness
and generalization capability. In this paper, we propose a novel Contrastive
Variational Information Bottleneck framework (called CVIB) to reduce spurious
correlations for ABSA. The proposed CVIB framework is composed of an original
network and a self-pruned network, and these two networks are optimized
simultaneously via contrastive learning. Concretely, we employ the Variational
Information Bottleneck (VIB) principle to learn an informative and compressed
network (self-pruned network) from the original network, which discards the
superfluous patterns or spurious correlations between input features and
prediction labels. Then, self-pruning contrastive learning is devised to pull
together semantically similar positive pairs and push away dissimilar pairs,
where the representations of the anchor learned by the original and self-pruned
networks respectively are regarded as a positive pair while the representations
of two different sentences within a mini-batch are treated as a negative pair.
To verify the effectiveness of our CVIB method, we conduct extensive
experiments on five benchmark ABSA datasets and the experimental results show
that our approach achieves better performance than the strong competitors in
terms of overall prediction performance, robustness, and generalization
An explainable sequence-based deep learning predictor with applications to song recommendation and text classification.
Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy in recommendation systems, recent research has unveiled the importance of other significant aspects such as explainability and solving the cold start problem where a new user or item with no prior history of interactions joins an online platform. In this work, we propose a hybrid deep learning structure, called “SeER”, that uses collaborative filtering and deep sequence models on the MIDI content of songs for recommendation. Our approach aims to take advantage of the superior capabilities of re-current neural networks, the multidimensional time series aspect of songs, and the power of matrix factorization to: •provide more accurate personalized recommendations, •solve the item cold start problem which is in the case of where a new unrated song is added to the set of choices to recommend; and •generate a relevant explanation for a song recommendation using a novel explainability process we named “Segment Forward Propagation Explainability”. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation. In addition, we demonstrate how our explanation mechanism can be used with generic sequential data beyond music, namely unstructured free text in two application domains: sentiment classification of online user reviews and delineating potential child abuse instances from medical examination reports
Deep Memory Networks for Attitude Identification
We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.Comment: Accepted to WSDM'1
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