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View Code? Open in Web Editor NEWA Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
Hi, except for the 7 entity tags defined, I also find 2 new entity tags in the NER dataset: "Term" and "Physical". So how to deal with these two types? How to be consistent with the thesis experiment?
There is no Relation Tags and an additional "Abstract" NER tags
In your paper: " we manually annotate 726 articles, 29,096 sentences and over 100,000 characters in total ".But we found that 24165 / 1895 / 2837 sentences in train / dev / test. Their sum does not reach 20,906 sentences.
Also, in the train set. we found some extremely long sentences which are separated by ".". Does it mean that “.” is the “。”.
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Thanks for sharing the interesting data set.
This data is annotated on passage level. The released data set, the words are separated by sentence.
I wonder would you kindly release a version with passage separator?
The format may look like follows
w0 tag0
w1 tag1
sentence_separator
w2 tag2
w2 tag3
passage_separator # end of a passage
w tag
w tag
...
it is a good ideas ,can you share the code of this experiment?[email protected]
Hello, I got some questions about the NER Dataset.
I found that there are 7 kinds of entities according to your paper.
However after processing with your Dataset, I found 10 kinds of entities.
The three additional entities are Term, Physical and ABstract(only three phrases, I suppose it should be Abstract……).
Also, I found some extremely long sentences which are not separated by ".".
And other sentences are separated by "。"
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