164,372 research outputs found
Active Discriminative Text Representation Learning
We propose a new active learning (AL) method for text classification with
convolutional neural networks (CNNs). In AL, one selects the instances to be
manually labeled with the aim of maximizing model performance with minimal
effort. Neural models capitalize on word embeddings as representations
(features), tuning these to the task at hand. We argue that AL strategies for
multi-layered neural models should focus on selecting instances that most
affect the embedding space (i.e., induce discriminative word representations).
This is in contrast to traditional AL approaches (e.g., entropy-based
uncertainty sampling), which specify higher level objectives. We propose a
simple approach for sentence classification that selects instances containing
words whose embeddings are likely to be updated with the greatest magnitude,
thereby rapidly learning discriminative, task-specific embeddings. We extend
this approach to document classification by jointly considering: (1) the
expected changes to the constituent word representations; and (2) the model's
current overall uncertainty regarding the instance. The relative emphasis
placed on these criteria is governed by a stochastic process that favors
selecting instances likely to improve representations at the outset of
learning, and then shifts toward general uncertainty sampling as AL progresses.
Empirical results show that our method outperforms baseline AL approaches on
both sentence and document classification tasks. We also show that, as
expected, the method quickly learns discriminative word embeddings. To the best
of our knowledge, this is the first work on AL addressing neural models for
text classification.Comment: This paper got accepted by AAAI 201
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vector
representation for each node, which encodes network structure and/or node
content information, such that off-the-shelf machine learning algorithms can be
easily applied to the vector-format node representations for network analysis.
However, the learned dense vector representations are inefficient for
large-scale similarity search, which requires to find the nearest neighbor
measured by Euclidean distance in a continuous vector space. In this paper, we
propose a search efficient binary network embedding algorithm called BinaryNE
to learn a sparse binary code for each node, by simultaneously modeling node
context relations and node attribute relations through a three-layer neural
network. BinaryNE learns binary node representations efficiently through a
stochastic gradient descent based online learning algorithm. The learned binary
encoding not only reduces memory usage to represent each node, but also allows
fast bit-wise comparisons to support much quicker network node search compared
to Euclidean distance or other distance measures. Our experiments and
comparisons show that BinaryNE not only delivers more than 23 times faster
search speed, but also provides comparable or better search quality than
traditional continuous vector based network embedding methods
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