deepnl is a Python library for Natural Language Processing tasks based on
a Deep Learning neural network architecture.
The library currently provides tools for performing part-of-speech tagging, Named Entity tagging and Semantic Role Labeling.
deepnl also provides code for creating word embeddings from text, using
either the Language Model approach by [Collobert11], or Hellinger PCA,
as in [Lebret14].
It can also create sentiment specific word embeddings from a corpus of annotated Tweets.
If you use deepnl, please cite [Attardi] in your publications.
WARNING. There has been a change in file format for models since version 1.3.14. You will have to retrain them to use with later versions.
Download the code or clone the repository on your machine with:
$ git clone https://github.com/attardi/deepnl.git
Ensure that you have the dependencies mentioned below, then proceed to the build process described below.
deepnl requires numpy and Eigen.
A C++ compiler is also needed for compiling the C++ extensions it uses,
produced with Cython.
The generated .cpp files are already provided with deepnl, but you
will need Cython if you want to develop or modify the C++ extensions.
To compile the library, run:
$ python2 setup.py build
This will invoke the C++ compiler to compile the code on your platform.
You can run the scripts directly from the bin directory, or you can
install them by calling:
$ sudo python setup.py install
If Cython gets invoked and raises error, force an update on the file timestamps, with:
$ touch deepnl/*.cpp
deepnl can be used both as a Python library or through command line scripts.
You can use deepnl as a library in Python code as follows, where
filename is the name of the file containing the model produced through training:
>>>fromdeepnl.taggerimportTagger>>>tagger=Tagger.load(open(filename))
>>>sent='The quick brown fox jumped over the lazy dog .'>>>tagger.tag_sequence(sent.split(), return_tokens=True)
[[(u'The', u'DT'), (u'quick', u'JJ'), (u'brown', u'JJ'), (u'fox', u'NN'), (u'jumped', u'VBD'), (u'over', u'IN'), (u'the', u'DT'), (u'lazy', u'JJ'), (u'dog', u'NN'), (u'.', '.')]]Class Tagger is a generic interface for sequence taggers and provides a
method tag_sequence for tagging a sentence.
A sentence is represented as a list of tokens.
Class Tagger can be used directly for performing POS tagging.
Two specializations are provided: NerTagger`, for Named Entity tagging and
``SrlTagger for Semantic Role Labeling.
The output of tag_sequence is normally a list of tuples, representing
tokens with their associated tags. In the case of POS tagging, the tags are
just the POS tags of each token; in case of NerTagger the tags are in
IOB notation for representing subsequences, while in the case of
SrlTagger the output is more complex.
deepnl provides scripts for tagging text or training new models.
They are present in the bin subdirectory where you downloaded the code. If you did not install them, you can invoke them directly from there.
Call them with option -h or --help to obtain details on their usage.
The scripts expect tokenized input, one token per line, with an empty line to separate sentences.
When training, the token attributes are supplied in TSV (tab separated values) format.
Here is an example of POS tagging, using a previously trained model from file pos.dnn:
$ dl-pos.py pos.dnn
The
quick
brown
fox
jumped
over
the
lazy
dog
.
The DT
quick JJ
brown JJ
fox NN
jumped VBD
over IN
the DT
lazy JJ
dog NN
..The command dl-words.py allows creating word embeddings from a language
model built from a plain text corpus, properly tokenized.
The command dl-words-pca.py allows creating word embeddings from a
language model built from a plain text corpus, with the technique of Hellinger
PCA.
The command dl-sentiwords.py allows creating sentiment specific word
embeddings from a corpus of annotated Tweets.
The NER tagger replicates the performance of SENNA in the CoNLL 2003 benchmark.
The CoNLL-2003 shared task data can be downloaded from http://www.cnts.ua.ac.be/conll2003/ner/.
The train and test data must be cleaned and converted to the more recent IOB2 notation, by calling:
sed '/-DOCSTART-/,+1d' train | bin/toIOB.py | cut -f 1,2,4 > train.iob
sed '/-DOCSTART-/,+1d' testa | bin/toIOB.py | cut -f 1,2,4 > testa.iob
sed '/-DOCSTART-/,+1d' testb | bin/toIOB.py | cut -f 1,2,4 > testb.iob
cat train.iob testa.iob > train+dev.iobAssuming that the SENNA distribution is in directory senna, the embeddings
and vocabulary from SENNA can be used:
cp -p senna/embeddings/embeddings.txt vectors.txt
cp -p senna/hash/words.lst vocab.txtThe gazetters from SENNA can be used to produce a single entity list as follows:
iconv -f ISO-8859-1 -t UTF-8 < senna/hash/ner.loc.lst | awk '{printf "LOC\t%s\n", $$0}'> eng.list
iconv -f ISO-8859-1 -t UTF-8 < senna/hash/ner.misc.lst | awk '{printf "MISC\t%s\n", $$0}'>> eng.list
iconv -f ISO-8859-1 -t UTF-8 < senna/hash/ner.org.lst | awk '{printf "ORG\t%s\n", $$0}'>> eng.list
iconv -f ISO-8859-1 -t UTF-8 < senna/hash/ner.per.lst | awk '{printf "PER\t%s\n", $$0}'>> eng.listYou also need the list of suffixes:
cp -p senna/hash/suffix.lst suffix.lstThe tagger can then be trained as follows:
bin/dl-ner.py ner.dnn -t train+dev.iob \
--vocab vocab.txt --vectors vectors.txt \
--caps --suffix --suffixes suffix.lst --gazetteer eng.list \
-e 40 --variant senna \
-l 0.01 -w 5 -n 300 -vThe benchmark can be run as:
bin/dl-ner.py ner.dnn < testb.iob > testb.out.iobThe results I achieved are:
processed 46435 tokens with 5648 phrases; found: 5640 phrases; correct: 5031. accuracy: 97.62%; precision: 89.20%; recall: 89.08%; FB1: 89.14 LOC: precision: 93.30%; recall: 91.01%; FB1: 92.14 MISC: precision: 78.24%; recall: 77.35%; FB1: 77.79 ORG: precision: 84.59%; recall: 87.24%; FB1: 85.89 PER: precision: 94.71%; recall: 94.06%; FB1: 94.38
You can modify or extend the code just by adding them to the directory deepnl.
To compile the extension, use the same build process, but you will also need to have Cython installed.
The compiler will issue warnings about NumPy of the type:
/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]#warning "Using deprecated NumPy API, disable it by "
Simply disregard them, since currently there is no way to fix them, until the maintainers of Cython will decide to upgrade it to use the latest API.
Erick Fonseca developed nlpnet, a similar library, available at:
https://github.com/erickrf/nlpnet, which provided inspiration for deepnl.
| [Attardi] | Giuseppe Attardi. 2015. DeepNL: a Deep Learning NLP pipeline. Workshop on Vector Space Modeling for NLP, NAACL 2015, Denver, Colorado (June 5, 2015). |
| [Collobert11] | Ronan Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 12:2493-2537, 2011. |
| [Lebret14] | Rémi Lebret and Ronan Collobert. 2014. Word Embeddings through Hellinger PCA. EACL 2014: 482. |