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Hierarchical Attention Networks for Document Classification
TLDR
Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Expand
Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics
TLDR
The results show that automatic evaluation using unigram co-occurrences between summary pairs correlates surprising well with human evaluations, based on various statistical metrics; while direct application of the BLEU evaluation procedure does not always give good results. Expand
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
TLDR
A novel neutral network architecture is introduced that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF, thus making it applicable to a wide range of sequence labeling tasks. Expand
OntoNotes: The 90% Solution
TLDR
It is described the OntoNotes methodology and its result, a large multilingual richly-annotated corpus constructed at 90% interannotator agreement, which will be made available to the community during 2007. Expand
RACE: Large-scale ReAding Comprehension Dataset From Examinations
TLDR
The proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models and the ceiling human performance. Expand
Self-Training With Noisy Student Improves ImageNet Classification
We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. OnExpand
Unsupervised Data Augmentation for Consistency Training
TLDR
A new perspective on how to effectively noise unlabeled examples is presented and it is argued that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. Expand
Determining the Sentiment of Opinions
TLDR
A system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion and another module for determining word sentiment and another for combining sentiments within a sentence is presented. Expand
Learning surface text patterns for a Question Answering System
TLDR
This paper has developed a method for learning an optimal set of surface text patterns automatically from a tagged corpus, and calculates the precision of each pattern, and the average precision for each question type. Expand
Government 2.0: Making connections between citizens, data and government
TLDR
The digital government or electronic government (e-government) has started as a new form of public organization that supports and redefines the existing and new information, communication and transaction-related interactions with stakeholders with the purpose of improving government performance and processes. Expand
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