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NLPCC2016
Text
NLP
|...
License: Custom

Overview

Word is the fundamental unit in natural language understanding. However, Chinese sentences
consists of the continuous Chinese characters without natural delimiters. Therefore, Chinese
word segmentation has become the first mission of Chinese natural language processing, which
identifies the sequence of words in a sentence and marks the boundaries between words.

Different
with the popular used news dataset, we use more informal texts from Sina Weibo. The training
and test data consist of micro-blogs from various topics, such as finance, sports, entertainment,
and so on.

Data Collection

The data are collected from Sina Weibo. Both the training and test files are UTF-8 encoded.
Besides the training data, we also provide the background data, from which the training and
test data are drawn. The purpose of providing the background data is to find the more sophisticated
features by the unsupervised way.

Citation

@InProceedings{qiu2016overview,
  Title                    = {Overview of the {NLPCC-ICCPOL} 2016 Shared Task: Chinese Word
Segmentation for Micro-blog Texts},
  Author                   = {Xipeng Qiu and Peng Qian and Zhan Shi},
  Booktitle                = {Proceedings of The Fifth
Conference on Natural Language Processing and Chinese Computing \& The Twenty Fourth
International Conference on Computer Processing of Oriental Languages},
  Year                     = {2016}
}

License

Custom

Data Summary
Type
Text,
Amount
--
Size
38.67MB
Provided by
NLP Group at Fudan University
Fudan University is a major public research university in Shanghai, China. It is widely considered as one of the most prestigious and selective universities in China.
| Amount -- | Size 38.67MB
NLPCC2016
Text
NLP
License: Custom

Overview

Word is the fundamental unit in natural language understanding. However, Chinese sentences
consists of the continuous Chinese characters without natural delimiters. Therefore, Chinese
word segmentation has become the first mission of Chinese natural language processing, which
identifies the sequence of words in a sentence and marks the boundaries between words.

Different
with the popular used news dataset, we use more informal texts from Sina Weibo. The training
and test data consist of micro-blogs from various topics, such as finance, sports, entertainment,
and so on.

Data Collection

The data are collected from Sina Weibo. Both the training and test files are UTF-8 encoded.
Besides the training data, we also provide the background data, from which the training and
test data are drawn. The purpose of providing the background data is to find the more sophisticated
features by the unsupervised way.

Citation

@InProceedings{qiu2016overview,
  Title                    = {Overview of the {NLPCC-ICCPOL} 2016 Shared Task: Chinese Word
Segmentation for Micro-blog Texts},
  Author                   = {Xipeng Qiu and Peng Qian and Zhan Shi},
  Booktitle                = {Proceedings of The Fifth
Conference on Natural Language Processing and Chinese Computing \& The Twenty Fourth
International Conference on Computer Processing of Oriental Languages},
  Year                     = {2016}
}

License

Custom

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