An implementation of the Stanford Multi-Pass Sieve Coreference System.
Get the repository and install the required packages:
$ git clone https://github.com/andreasvc/dutchcoref.git
$ cd dutchcoref
$ pip3 install -r requirements.txt
Unless you are working on an already parsed corpus, you will want to install the Alpino parser.
To get parse tree visualizations in the HTML output, install https://github.com/andreasvc/disco-dop/
Download Top_eerste_voornamen_NL_2010.csv
from https://www.meertens.knaw.nl/nvb/ (click on "Veelgestelde vragen"
and then on the bottom "Klik hier").
Unzip it and put the csv file in the data/ directory.
Clone this repository under same parent folder as this repository:
~/code/dutchcoref $ cd ..
~/code $ git clone https://bitbucket.org/robvanderg/groref.git
Download the data from http://www.lsi.upc.edu/~esapena/downloads/index.php?id=1
Apply the Unicode fix in fixsemeval2010.sh.
The directory data/semeval2010NLdevparses contains Alpino parses for the
Dutch development set of this task.
See coref.py --help for command line options.
$ cat /tmp/example.txt
' Ik ben de directeur van Fecalo , van hierachter , ' zei hij .
' Mag ik u iets vragen ? '
Ik vroeg hem binnen te komen .
$ mkdir example
$ cat example.txt | Alpino number_analyses=1 end_hook=xml -parse -flag treebank example
[...]
$ python3 coref.py --verbose /tmp/example
$ python3 coref.py --fmt=booknlp /tmp/example
#begin document (example);
1 6-1 1 ' ' LET() 5 punct - 14 - B -
2 6-1 2 Ik ik VNW(pers,pron,nomin,vol,1,ev) 5 nsubj - 14 - I (0)
3 6-1 3 ben zijn WW(pv,tgw,ev) 5 cop - 14 - I -
4 6-1 4 de de LID(bep,stan,rest) 5 det - 14 - I (0
5 6-1 5 directeur directeur N(soort,ev,basis,zijd,stan) 0 root - 14 - I 0
6 6-1 6 van van VZ(init) 7 case - 14 - I 0
7 6-1 7 Fecalo Fecalo N(eigen,ev,basis,zijd,stan) 5 nmod ORG 14 - I 0)|(1)
8 6-1 8 , , LET() 5 punct - 14 - I -
9 6-1 9 van van VZ(init) 10 case - 14 - I -
10 6-1 10 hierachter hierachter BW() 5 nmod - 14 - I -
11 6-1 11 , , LET() 5 punct - 14 - I -
12 6-1 12 ' ' LET() 5 punct - 14 - I -
13 6-1 13 zei zeggen WW(pv,verl,ev) 5 parataxis - - - O -
14 6-1 14 hij hij VNW(pers,pron,nomin,vol,3,ev,masc) 13 nsubj - - - O (0)
15 6-1 15 . . LET() 5 punct - - - O -
16 6-2 1 ' ' LET() 6 punct - 14 - B -
17 6-2 2 Mag mogen WW(pv,tgw,ev) 6 aux - 14 - I -
18 6-2 3 ik ik VNW(pers,pron,nomin,vol,1,ev) 6 nsubj - 14 - I (0)
19 6-2 4 u u VNW(pers,pron,nomin,vol,2b,getal) 6 iobj - 14 - I (5)
20 6-2 5 iets iets VNW(onbep,pron,stan,vol,3o,ev) 6 obj - 14 - I -
21 6-2 6 vragen vragen WW(inf,vrij,zonder) 0 root - 14 - I -
22 6-2 7 ? ? LET() 6 punct - 14 - I -
23 6-2 8 ' ' LET() 6 punct - 14 - I -
24 7-1 1 Ik ik VNW(pers,pron,nomin,vol,1,ev) 2 nsubj - - - O (6)
25 7-1 2 vroeg vragen WW(pv,verl,ev) 0 root - - - O -
26 7-1 3 hem hem VNW(pers,pron,obl,vol,3,ev,masc) 2 iobj - - - O (0)
27 7-1 4 binnen binnen VZ(fin) 6 compound:prt - - - O -
28 7-1 5 te te VZ(init) 6 mark - - - O -
29 7-1 6 komen binnen_komen WW(inf,vrij,zonder) 2 xcomp - - - O -
30 7-1 7 . . LET() 2 punct - - - O -
#end document
UPDATE: use https://github.com/andreasvc/berkeley-coreference-analyser
The following creates lots of output; scroll to the end for the error analysis. Mention boundaries and links are printed in green if they are correct, yellow if in gold but missing from output, and red if in output but not in gold.
$ ls mydocument/
1.xml 2.xml [...]
$ python3 coref.py mydocument/ --gold=mydocument.conll --verbose | less -R
alternatively, use the HTML visualization and view the results in your favorite browser:
$ python3 coref.py mydocument/ --gold=mydocument.conll --verbose --fmt=html >output.html
See https://andreasvc.github.io/voskuil.html for an example of the HTML visualization.
For further analysis with other tools, use the option `--outputprefix' to dump information on clusters, mentions, links and quotations:
$ python3 coref.py mydocument/ --fmt=booknlp --outputprefix=output
This creates the files output.{mentions,clusters,links,quotes}.tsv (tabular format)
output.conll (--fmt), and output.icarus (ICARUS allocation format).
Make sure you don't overwrite the gold standard conll file!
Get the scorer script: https://github.com/andreasvc/coval which is an improved version of: https://github.com/ns-moosavi/coval
$ python3 coref.py mydocument/ --fmt=conll2012 >output.conll
$ python3 ../coval/scorer.py mydocument.conll output.conll
mentions Recall: 90.52 Precision: 81.43 F1: 85.73
muc Recall: 79.44 Precision: 74.43 F1: 76.85
bcub Recall: 51.72 Precision: 55.65 F1: 53.61
ceafe Recall: 66.64 Precision: 46.58 F1: 54.83
lea Recall: 49.48 Precision: 52.74 F1: 51.05
CoNLL score: 61.77
With --fmt=booknlp, the output contains the following columns:
- Global token number
- Sentence ID
- Token number within sentence
- Token
- Lemma
- Rich POS tag (including morphological features)
- UD parent token (ID as in column 3)
- UD dependency label
- Named entity class (PER, ORG, LOC, ...)
- Speaker ID (if a speaker is found, every token in a direct speech utterance is assigned the speaker ID; the ID is the cluster ID of the speaker)
- Similar as above, but for addressee.
- Whether token is part of direct speech (B, I) or not (O)
- Coreference cluster in CoNLL notation
The web demo accepts short pieces of text, takes care of parsing, and presents
a visualization of coreference results. Requires a running instance of
alpiner.
Run with python3 web.py
- Preprocess, tokenize and parse a text with Alpino to get a directory of parse trees in XML files.
- Run coreference resolution on the parse trees:
python3 coref.py --fmt=conll2012 path/to/parses/ > text.conll(Forward slashes are required, also on Windows). - Get the latest stable release of CorefAnnotator.
Run it with
java -jar CorefAnnotator-1.9.2-full.jar - Import the
.conllfile (CoNLL 2012 button under "Import from other formats"). - Read the annotation guidelines in this repository
- Correct the annotation; save regularly (in the .xmi format used by CorefAnnotator)
- When done, export to CoNLL 2012 format
- The CoNLL 2012 file exported by CorefAnnotator does not contain POS tags and parse trees;
to add those, run
addparsebits.py alpino text.conll path/to/parses/
This code base is a Dutch implementation of the Stanford Multi-Pass Sieve Coreference System for English:
Heeyoung Lee, Angel Chang, Yves Peirsman, Nathanael Chambers, Mihai Surdeanu, and Dan Jurafsky. Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics, 39 (4):885–916, 2013. http://aclweb.org/anthology/J13-4004.pdf
See also these previous implementations https://bitbucket.org/robvanderg/groref and https://github.com/antske/coref_draft
The UD conversion uses a slightly modified version of https://github.com/gossebouma/lassy2ud
The number & gender data is derived from:
Shane Bergsma and Dekang Lin (2006). Bootstrapping Path-Based Pronoun Resolution, In Proceedings of COLING/ACL. http://www.clsp.jhu.edu/~sbergsma/Gender/