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Source code: Neural Architectures for Nested NER through Linearization
======================================================================
Jana Straková, Milan Straka and Jan Hajič
https://aclweb.org/anthology/papers/P/P19/P19-1527/
{strakova,straka,hajic}@ufal.mff.cuni.cz

License
-------

Copyright 2019 Institute of Formal and Applied Linguistics, Faculty of
Mathematics and Physics, Charles University, Czech Republic.

This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.

Please cite as:
---------------

@inproceedings{strakova-etal-2019-neural,
    title = {{Neural Architectures for Nested {NER} through Linearization}},
    author = {Jana Strakov{\'a} and Milan Straka and Jan Haji\v{c}},
    booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
    month = jul,
    year = {2019},
    address = {Florence, Italy},
    publisher = {Association for Computational Linguistics},
    url = {https://www.aclweb.org/anthology/P19-1527},
    pages = {5326--5331},
}

How to run the tagger
---------------------

1. Install requirements

pip install -r requirements.txt

2. Download the data

ACE-2004: https://catalog.ldc.upenn.edu/LDC2005T09
ACE-2005: https://catalog.ldc.upenn.edu/LDC2006T06
GENIA: http://www.geniaproject.org/

3. Create inputs

The input of the tagger is in the CoNLL-2003 BILOU format. CoNLL-2003 shared
task data format is described here:
https://www.clips.uantwerpen.be/conll2003/ner/ . BILOU format is described
here (Ratinov and Roth, 2009): https://www.aclweb.org/anthology/W09-1119 .

The input format is a CoNLL format, with one token per line, sentences
delimited by empty line. For each token, columns are separated by tabs. First
column is the surface token, second column is lemma, third column is a POS tag
and fourth column is the BILOU encoded NE label.

For flat corpora (e.g. CoNLL-2003 English and German), the fourth column bears
exactly one NE label, e.g. (example from CoNLL-2003 English):

-DOCSTART-      -docstart-      NN      O

EU      EU      NNP     U-ORG
rejects reject  VBZ     O
German  german  JJ      U-MISC
call    call    NN      O
to      to      TO      O
boycott boycott VB      O
British british JJ      U-MISC
lamb    lamb    NN      O
.       .       .       O

For nested NE corpora, the NE tags are linearized (flattened) according to
rules described in the paper, e.g. (example from ACE-2004):

The	the	DT	B-GPE
Chinese	chinese	JJ	I-GPE|U-GPE
government	government	NN	L-GPE
and	and	CC	O
the	the	DT	B-GPE
Australian	australian	JJ	I-GPE|U-GPE
government	government	NN	L-GPE
signed	sign	VBD	O
an	an	DT	O
agreement	agreement	NN	O
today	today	NN	O
,	,	,	O
wherein	wherein	WRB	O
the	the	DT	B-GPE
Australian	australian	JJ	I-GPE|U-GPE
party	party	NN	L-GPE
would	would	MD	O
provide	provide	VB	O
China	China	NNP	U-GPE
with	with	IN	O
a	a	DT	O
preferential	preferential	JJ	O
financial	financial	JJ	O
loan	loan	NN	O
of	of	IN	O
150	150	CD	O
million	million	CD	O
Australian	australian	JJ	U-GPE
dollars	dollar	NNS	O
.	.	.	O

The lemmatization and POS tagging can be done with e.g. UDPipe
(http://ufal.mff.cuni.cz/udpipe) or with MorphoDiTa
(http://ufal.mff.cuni.cz/morphodita) or with any tool of your choice. If you
don't have any POS tagger or lemmatizer, simply fill the respective columns
with dummy (e.g. "_").

4. Get word embeddings

- word2vec,
- FastText,
- BERT,
- ELMo,
- Flair

from sources described in the paper. The input formats are:

- word2vec: The native word2vec text file. 
- FastText: The native FastText binary.
- contextualized embeddings (BERT, ELMo, Flair): A text file with one token per
  line, first column is the token, all other columns are the vector real valued
  numbers; columns separated with space. The format is readable for human eyes,
  but quite large, sorry for the inconvenience. The per-token BERT
  contextualized word embeddings are created as an average of all token
  corresponding BERT subowords. The ELMo and Flair are generated using this
  code: https://github.com/zalandoresearch/flair. 

You can also run the tagger without pretrained word embeddings just with
end-to-end word embeddings and character-level embeddings (created inside the
tagger), or with a subset of the above mentioned pretrained word embeddings.

5. Run the tagger

Usage example:

./tagger.py --corpus=CoNLL_en --train_data=conll_en/train_dev_bilou.conll --test_data=conll_en/test_bilou.conll --decoding=seq2seq --epochs=10:1e-3,8:1e-4 --form_wes_model=word_embeddings/conll_en_form.txt --lemma_wes_model=word_embeddings/conll_en_lemma.txt --bert_embeddings_train=bert_embeddings/conll_en_train_dev_bert_large_embeddings.txt --bert_embeddings_test=bert_embeddings/conll_en_test_bert_large_embeddings.txt --flair_train=flair_embeddings/conll_en_train_dev.txt --flair_test=flair_embeddings/conll_en_test.txt --elmo_train=elmo_embeddings/conll_en_train_dev.txt --elmo_test=elmo_embeddings/conll_en_test.txt --name=seq2seq+ELMo+BERT+Flair

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