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XuezheMax / NeuroNLP2

Deep neural models for core NLP tasks (Pytorch version)
GNU General Public License v3.0
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About the datas in the example #2

Closed chzeze closed 7 years ago

chzeze commented 7 years ago

Hi Max, could you share the datas which is used in the example ? such as the datas in the path : data/conll2003/english/ or data/POS-penn/wsj/split1/ thanks.

XuezheMax commented 7 years ago

Hi, I am sorry that due to the data licence, I cannot share the data publicly. But I can show examples of the data to illustrate the format of the data files.

Part-of-speech tagging and dependency parsing data follows the CoNLL-X format:

1 The DT DT 2 NMOD 2 company NN NN 3 SUB 3 had VB VBD 0 ROOT 4 sought VB VBN 3 VC 5 increases NN NNS 4 OBJ 6 totaling VB VBG 5 NMOD 7 $ $ $ 13 P 8 80.3 CD CD 7 AMOD 9 million CD CD 7 AMOD 10 , , , 13 P 11 or CC CC 13 NMOD 12 22 CD CD 13 NMOD 13 % NN NN 6 OBJ 14 . . . 3 P

1 The DT DT 2 NMOD 2 decision NN NN 3 SUB 3 was VB VBD 0 ROOT 4 announced VB VBN 3 VC 5 after IN IN 4 VMOD 6 trading NN NN 7 SUB 7 ended VB VBD 5 SBAR 8 . . . 3 P

The NER data examples are in the following:

1 CRICKET NNP I-NP O 2 - : O O 3 LEICESTERSHIRE NNP I-NP B-ORG 4 TAKE NNP I-NP O 5 OVER IN I-PP O 6 AT NNP I-NP O 7 TOP NNP I-NP O 8 AFTER NNP I-NP O 9 INNINGS NNP I-NP O 10 VICTORY NN I-NP O 11 . . O O

1 By IN I-PP O 2 stumps NNS I-NP O 3 Kent NNP B-NP B-ORG 4 had VBD I-VP O 5 reached VBN I-VP O 6 108 CD I-NP O 7 for IN I-PP O 8 three CD I-NP O 9 . . O O

chzeze commented 7 years ago

thank you very much