14,693 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
Neural Cross-Lingual Entity Linking
A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the English Wikipedia: to compare textual clues across
languages we need to compute similarity between textual fragments across
languages. In this paper, we propose a neural EL model that trains fine-grained
similarities and dissimilarities between the query and candidate document from
multiple perspectives, combined with convolution and tensor networks. Further,
we show that this English-trained system can be applied, in zero-shot learning,
to other languages by making surprisingly effective use of multi-lingual
embeddings. The proposed system has strong empirical evidence yielding
state-of-the-art results in English as well as cross-lingual: Spanish and
Chinese TAC 2015 datasets.Comment: Association for the Advancement of Artificial Intelligence (AAAI),
201
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given
case, which is helpful for legal assistant systems where the user input is fact
description. We argue that relevant law articles play an important role in this
task, and therefore propose an attention-based neural network method to jointly
model the charge prediction task and the relevant article extraction task in a
unified framework. The experimental results show that, besides providing legal
basis, the relevant articles can also clearly improve the charge prediction
results, and our full model can effectively predict appropriate charges for
cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology
Ideological leanings of an individual can often be gauged by the sentiment
one expresses about different issues. We propose a simple framework that
represents a political ideology as a distribution of sentiment polarities
towards a set of topics. This representation can then be used to detect
ideological leanings of documents (speeches, news articles, etc.) based on the
sentiments expressed towards different topics. Experiments performed using a
widely used dataset show the promise of our proposed approach that achieves
comparable performance to other methods despite being much simpler and more
interpretable.Comment: Presented at EMNLP Workshop on Computational Approaches to
Subjectivity, Sentiment & Social Media Analysis, 201
Hierarchical Exploration for Accelerating Contextual Bandits
Contextual bandit learning is an increasingly popular approach to optimizing
recommender systems via user feedback, but can be slow to converge in practice
due to the need for exploring a large feature space. In this paper, we propose
a coarse-to-fine hierarchical approach for encoding prior knowledge that
drastically reduces the amount of exploration required. Intuitively, user
preferences can be reasonably embedded in a coarse low-dimensional feature
space that can be explored efficiently, requiring exploration in the
high-dimensional space only as necessary. We introduce a bandit algorithm that
explores within this coarse-to-fine spectrum, and prove performance guarantees
that depend on how well the coarse space captures the user's preferences. We
demonstrate substantial improvement over conventional bandit algorithms through
extensive simulation as well as a live user study in the setting of
personalized news recommendation.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
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