2 research outputs found
A Generic Online Parallel Learning Framework for Large Margin Models
To speed up the training process, many existing systems use parallel
technology for online learning algorithms. However, most research mainly focus
on stochastic gradient descent (SGD) instead of other algorithms. We propose a
generic online parallel learning framework for large margin models, and also
analyze our framework on popular large margin algorithms, including MIRA and
Structured Perceptron. Our framework is lock-free and easy to implement on
existing systems. Experiments show that systems with our framework can gain
near linear speed up by increasing running threads, and with no loss in
accuracy
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
Named Entity Recognition and Relation Extraction for Chinese literature text
is regarded as the highly difficult problem, partially because of the lack of
tagging sets. In this paper, we build a discourse-level dataset from hundreds
of Chinese literature articles for improving this task. To build a high quality
dataset, we propose two tagging methods to solve the problem of data
inconsistency, including a heuristic tagging method and a machine auxiliary
tagging method. Based on this corpus, we also introduce several widely used
models to conduct experiments. Experimental results not only show the
usefulness of the proposed dataset, but also provide baselines for further
research. The dataset is available at
https://github.com/lancopku/Chinese-Literature-NER-RE-Datase