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Natural language Understanding Toolkit
C Python
Latest commit dd654c8 @pprett update link to corpus


Natural language Understanding Toolkit



To install nut you need:

  • Python 2.5 or 2.6
  • Numpy (>= 1.1)
  • Sparsesvd (>= 0.1.4) [1] (only CLSCL)


To clone the repository run,

git clone git://

To build the extension modules inplace run,

python build_ext --inplace

Add project to python path,

export PYTHONPATH=$PYTHONPATH:$HOME/workspace/nut



An implementation of Cross-Language Structural Correspondence Learning (CLSCL). See [Prettenhofer2010] for a detailed description and [Prettenhofer2011] for more experiments and enhancements.

The data for cross-language sentiment classification that has been used in the above study can be found here [2].


Training script for CLSCL. See ./clscl_train --help for further details.


$ ./clscl_train en de cls-acl10-processed/en/books/train.processed cls-acl10-processed/en/books/unlabeled.processed cls-acl10-processed/de/books/unlabeled.processed cls-acl10-processed/dict/en_de_dict.txt model.bz2 --phi 30 --max-unlabeled=50000 -k 100 -m 450 --strategy=parallel

|V_S| = 64682
|V_T| = 106024
|V| = 170706
|s_train| = 2000
|s_unlabeled| = 50000
|t_unlabeled| = 50000
debug: DictTranslator contains 5012 translations.
mutualinformation took 5.624 sec
select_pivots took 7.197 sec
|pivots| = 450
create_inverted_index took 59.353 sec
Run joblib.Parallel
[Parallel(n_jobs=-1)]: Done   1 out of 450 |elapsed:    9.1s remaining: 67.8min
[Parallel(n_jobs=-1)]: Done   5 out of 450 |elapsed:   15.2s remaining: 22.6min
[Parallel(n_jobs=-1)]: Done 449 out of 450 |elapsed: 14.5min remaining:    1.9s
train_aux_classifiers took 881.803 sec
density: 0.1154
Ut.shape = (100,170706)
learn took 903.588 sec
project took 175.483 sec


If you have access to a hadoop cluster, you can use --strategy=hadoop to train the pivot classifiers even faster, however, make sure that the hadoop nodes have Bolt (feature-mask branch) [3] installed.


Prediction script for CLSCL.


$ ./clscl_predict cls-acl10-processed/en/books/train.processed model.bz2 cls-acl10-processed/de/books/test.processed 0.01
|V_S| = 64682
|V_T| = 106024
|V| = 170706
load took 0.681 sec
load took 0.659 sec
classes = {negative,positive}
project took 2.498 sec
project took 2.716 sec
project took 2.275 sec
project took 2.492 sec
ACC: 83.05

Named-Entity Recognition

A simple greedy left-to-right sequence labeling approach to named entity recognition (NER).

pre-trained models

We provide pre-trained named entity recognizers for place, person, and organization names in English and German. To tag a sentence simply use:

>>> from import compressed_load
>>> from nut.util import WordTokenizer

>>> tagger = compressed_load("model_demo_en.bz2")
>>> tokenizer = WordTokenizer()
>>> tokens = tokenizer.tokenize("Peter Prettenhofer lives in Austria .")

>>> # see tagger.tag.__doc__ for input format
>>> sent = [((token, "", ""), "") for token in tokens]
>>> g = tagger.tag(sent)  # returns a generator over tags
>>> print(" ".join(["/".join(tt) for tt in zip(tokens, g)]))
Peter/B-PER Prettenhofer/I-PER lives/O in/O Austria/B-LOC ./O

You can also use the convenience demo script

$ python model_en_v1.bz2

The feature detector modules for the pre-trained models are and and can be found in the package nut.ner.features. In addition to baseline features (word presence, shape, pre-/suffixes) they use distributional features (brown clusters), non-local features (extended prediction history), and gazetteers (see [Ratinov2009]). The models have been trained on CoNLL03 [4]. Both models use neither syntactic features (e.g. part-of-speech tags, chunks) nor word lemmas, thus, minimizing the required pre-processing. Both models provide state-of-the-art performance on the CoNLL03 shared task benchmark for English [Ratinov2009]:

processed 46435 tokens with 4946 phrases; found: 4864 phrases; correct: 4455.
accuracy:  98.01%; precision:  91.59%; recall:  90.07%; FB1:  90.83
              LOC: precision:  91.69%; recall:  90.53%; FB1:  91.11  1648
              ORG: precision:  87.36%; recall:  85.73%; FB1:  86.54  1630
              PER: precision:  95.84%; recall:  94.06%; FB1:  94.94  1586

and German [Faruqui2010]:

processed 51943 tokens with 2845 phrases; found: 2438 phrases; correct: 2168.
accuracy:  97.92%; precision:  88.93%; recall:  76.20%; FB1:  82.07
              LOC: precision:  87.67%; recall:  79.83%; FB1:  83.57  957
              ORG: precision:  82.62%; recall:  65.92%; FB1:  73.33  466
              PER: precision:  93.00%; recall:  78.02%; FB1:  84.85  1015

To evaluate the German model on the out-domain data provided by [Faruqui2010] use the raw flag (-r) to write raw predictions (without B- and I- prefixes):

./ner_predict -r model_de_v1.bz2 clner/de/europarl/test.conll - | clner/scripts/conlleval -r
loading tagger... [done]
use_eph:  True
use_aso:  False
processed input in 40.9214s sec.
processed 110405 tokens with 2112 phrases; found: 2930 phrases; correct: 1676.
accuracy:  98.50%; precision:  57.20%; recall:  79.36%; FB1:  66.48
              LOC: precision:  91.47%; recall:  71.13%; FB1:  80.03  563
              ORG: precision:  43.63%; recall:  83.52%; FB1:  57.32  1673
              PER: precision:  62.10%; recall:  83.85%; FB1:  71.36  694

Note that the above results cannot be compared directly to the resuls of [Faruqui2010] since they use a slighly different setting (incl. MISC entity).


Training script for NER. See ./ner_train --help for further details.

To train a conditional markov model with a greedy left-to-right decoder, the feature templates of [Rationov2009]_ and extended prediction history (see [Ratinov2009]) use:

./ner_train clner/en/conll03/train.iob2 model_rr09.bz2 -f rr09 -r 0.00001 -E 100 --shuffle --eph
Feature extraction

min count:  1
use eph:  True
build_vocabulary took 24.662 sec
feature_extraction took 25.626 sec
creating training examples... build_examples took 42.998 sec

num examples: 203621
num features: 553249
num classes: 9
classes:  ['I-LOC', 'B-ORG', 'O', 'B-PER', 'I-PER', 'I-MISC', 'B-MISC', 'I-ORG', 'B-LOC']
reg: 0.00001000
epochs: 100
9 models trained in 239.28 seconds.
train took 282.374 sec


You can use the prediction script to tag new sentences formatted in CoNLL format and write the output to a file or to stdout. You can pipe the output directly to conlleval to assess the model performance:

./ner_predict model_rr09.bz2 clner/en/conll03/test.iob2 - | clner/scripts/conlleval
loading tagger... [done]
use_eph:  True
use_aso:  False
processed input in 11.2883s sec.
processed 46435 tokens with 5648 phrases; found: 5605 phrases; correct: 4799.
accuracy:  96.78%; precision:  85.62%; recall:  84.97%; FB1:  85.29
              LOC: precision:  87.29%; recall:  88.91%; FB1:  88.09  1699
             MISC: precision:  79.85%; recall:  75.64%; FB1:  77.69  665
              ORG: precision:  82.90%; recall:  78.81%; FB1:  80.80  1579
              PER: precision:  88.81%; recall:  91.28%; FB1:  90.03  1662


[4]For German we use the updated version of CoNLL03 by Sven Hartrumpf.
[Prettenhofer2010]Prettenhofer, P. and Stein, B., Cross-language text classification using structural correspondence learning. In Proceedings of ACL '10.
[Prettenhofer2011]Prettenhofer, P. and Stein, B., Cross-lingual adaptation using structural correspondence learning. ACM TIST (to appear). [preprint]
[Ratinov2009](1, 2, 3) Ratinov, L. and Roth, D., Design challenges and misconceptions in named entity recognition. In Proceedings of CoNLL '09.
[Faruqui2010](1, 2, 3) Faruqui, M. and Padó S., Training and Evaluating a German Named Entity Recognizer with Semantic Generalization. In Proceedings of KONVENS '10

Developer Notes

  • If you copy a new version of bolt into the externals directory make sure to run cython on the *.pyx files. If you fail to do so you will get a PickleError in multiprocessing.
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