8,282 research outputs found
Molding CNNs for text: non-linear, non-consecutive convolutions
The success of deep learning often derives from well-chosen operational
building blocks. In this work, we revise the temporal convolution operation in
CNNs to better adapt it to text processing. Instead of concatenating word
representations, we appeal to tensor algebra and use low-rank n-gram tensors to
directly exploit interactions between words already at the convolution stage.
Moreover, we extend the n-gram convolution to non-consecutive words to
recognize patterns with intervening words. Through a combination of low-rank
tensors, and pattern weighting, we can efficiently evaluate the resulting
convolution operation via dynamic programming. We test the resulting
architecture on standard sentiment classification and news categorization
tasks. Our model achieves state-of-the-art performance both in terms of
accuracy and training speed. For instance, we obtain 51.2% accuracy on the
fine-grained sentiment classification task
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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