1,399 research outputs found
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
HoloDetect: Few-Shot Learning for Error Detection
We introduce a few-shot learning framework for error detection. We show that
data augmentation (a form of weak supervision) is key to training high-quality,
ML-based error detection models that require minimal human involvement. Our
framework consists of two parts: (1) an expressive model to learn rich
representations that capture the inherent syntactic and semantic heterogeneity
of errors; and (2) a data augmentation model that, given a small seed of clean
records, uses dataset-specific transformations to automatically generate
additional training data. Our key insight is to learn data augmentation
policies from the noisy input dataset in a weakly supervised manner. We show
that our framework detects errors with an average precision of ~94% and an
average recall of ~93% across a diverse array of datasets that exhibit
different types and amounts of errors. We compare our approach to a
comprehensive collection of error detection methods, ranging from traditional
rule-based methods to ensemble-based and active learning approaches. We show
that data augmentation yields an average improvement of 20 F1 points while it
requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages
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