liuning123 / deepnl

Deep Learning for Natural Language Processing

deepnl --- Deep Learning for Natural Language Processing

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.

Installation ===========

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.

Dependencies

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.

Build

To compile the library, run:

$ python setup.py build

This will invoce 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

Basic usage

deepnl can be used both as a Python library or through command line scripts.

Library usage

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:

>>> from deepnl.tagger import Tagger
>>> 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 andSrlTaggerfor Semantic Role Labeling. The output oftag_sequenceis 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 ofNerTaggerthe tags are inIOBnotation for representing subsequences, while in the case ofSrlTaggerthe output is more complex. Standalone scripts ------------------deepnlprovides 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-hor--helpto 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 filepos.dnn: .. code-block:: bash $ 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 . . Word Embeddings =============== The commanddl-words.pyallows creating word embeddings from a language model built from a plain text corpus, properly tokenized. The commanddl-words-pca.pyallows creating word embeddings from a language model built from a plain text corpus, with the technique of Hellinger PCA. The commanddl-sentiwords.pyallows creating *sentiment specific word embeddings* from a corpus of annotated Tweets. Benchmarks ========== 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: .. code-block:: bash 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.iob Assuming that the SENNA distribution is in directorysenna, the embeddings and vocabulary from SENNA can be used: .. code-block:: bash cp -p senna/embeddings/embeddings.txt vectors.txt cp -p senna/hash/words.lst vocab.txt The gazetters from SENNA can be used to produce a single entity list as follows: .. code-block:: bash 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.list You also need the list of suffixes: .. code-block:: bash cp -p senna/hash/suffix.lst suffix.lst The tagger can then be trained as follows: .. code-block:: bash 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 -v The benchmark can be run as: .. code-block:: bash bin/dl-ner.py ner.dnn < testb.iob > testb.out.iob The 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 Writing Extensions ================== You can modify or extend the code just by adding them to the directorydeepnl. 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. Credits ======= Erick Fonseca developednlpnet, a similar library, available at: https://github.com/erickrf/nlpnet, which provided inspiration fordeepnl``.

References

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.