Tag Archives: corpora

python

NLTK 2 Release Highlights

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NLTK 2.0.1, a.k.a NLTK 2, was recently released, and what follows is my favorite changes, new features, and highlights from the ChangeLog.

New Classifiers

The SVMClassifier adds support vector machine classification thru SVMLight with PySVMLight. This is a much needed addition to the set of supported classification algorithms. But even more interesting…

The SklearnClassifier provides a general interface to text classification with scikit-learn. While scikit-learn is still pre-1.0, it is rapidly becoming one of the most popular machine learning toolkits, and provides more advanced feature extraction methods for classification.

Github

NLTK has moved development and hosting to github, replacing google code and SVN. The primary motivation is to make new development easier, and already a Python 3 branch is under active development. I think this is great, since github makes forking & pull requests quite easy, and it’s become the de-facto “social coding” site.

Sphinx

Coinciding with the github move, the documentation was updated to use Sphinx, the same documentation generator used by Python and many other projects. While I personally like Sphinx and restructured text (which I used to write this post), I’m not thrilled with the results. The new documentation structure and NLTK homepage seem much less approachable. While it works great if you know exactly what you’re looking for, I worry that new/interested users will have a harder time getting started.

New Corpora

Since the 0.9.9 release, a number of new corpora and corpus readers have been added:

ChangeLog Highlights

And here’s a few final highlights:

The Future

I think NLTK’s ideal role is be a standard interface between corpora and NLP algorithms. There are many different corpus formats, and every algorithm has its own data structure requirements, so providing common abstract interfaces to connect these together is very powerful. It allows you to test the same algorithm on disparate corpora, or try multiple algorithms on a single corpus. This is what NLTK already does best, and I hope that becomes even more true in the future.

python

Analyzing Tagged Corpora and NLTK Part of Speech Taggers

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NLTK Trainer includes 2 scripts for analyzing both a tagged corpus and the coverage of a part-of-speech tagger.

Analyze a Tagged Corpus

You can get part-of-speech tag statistics on a tagged corpus using analyze_tagged_corpus.py. Here’s the tag counts for the treebank corpus:

$ python analyze_tagged_corpus.py treebank
loading nltk.corpus.treebank
100676 total words
12408 unique words
46 tags

  Tag      Count
=======  =========
#               16
$              724
''             694
,             4886
-LRB-          120
-NONE-        6592
-RRB-          126
.             3874
:              563
CC            2265
CD            3546
DT            8165
EX              88
FW               4
IN            9857
JJ            5834
JJR            381
JJS            182
LS              13
MD             927
NN           13166
NNP           9410
NNPS           244
NNS           6047
PDT             27
POS            824
PRP           1716
PRP$           766
RB            2822
RBR            136
RBS             35
RP             216
SYM              1
TO            2179
UH               3
VB            2554
VBD           3043
VBG           1460
VBN           2134
VBP           1321
VBZ           2125
WDT            445
WP             241
WP$             14
WRB            178
``             712
=======  =========

By default, analyze_tagged_corpus.py sorts by tags, but you can sort by the highest count using <span class="pre">--sort</span> count <span class="pre">--reverse</span>. You can also see counts for simplified tags using <span class="pre">--simplify_tags</span>:

$ python analyze_tagged_corpus.py treebank --simplify_tags
loading nltk.corpus.treebank
100676 total words
12408 unique words
31 tags

  Tag      Count
=======  =========
              7416
#               16
$              724
''             694
(              120
)              126
,             4886
.             3874
:              563
ADJ           6397
ADV           2993
CNJ           2265
DET           8192
EX              88
FW               4
L               13
MOD            927
N            19213
NP            9654
NUM           3546
P             9857
PRO           2698
S                1
TO            2179
UH               3
V             6000
VD            3043
VG            1460
VN            2134
WH             878
``             712
=======  =========

Analyze Tagger Coverage

You can analyze the coverage of a part-of-speech tagger against any corpus using analyze_tagger_coverage.py. Here’s the results for the treebank corpus using NLTK’s default part-of-speech tagger:

$ python analyze_tagger_coverage.py treebank
loading tagger taggers/maxent_treebank_pos_tagger/english.pickle
analyzing tag coverage of treebank with ClassifierBasedPOSTagger

  Tag      Found
=======  =========
#               16
$              724
''             694
,             4887
-LRB-          120
-NONE-        6591
-RRB-          126
.             3874
:              563
CC            2271
CD            3547
DT            8170
EX              88
FW               4
IN            9880
JJ            5803
JJR            386
JJS            185
LS              12
MD             927
NN           13166
NNP           9427
NNPS           246
NNS           6055
PDT             21
POS            824
PRP           1716
PRP$           766
RB            2800
RBR            130
RBS             33
RP             213
SYM              1
TO            2180
UH               3
VB            2562
VBD           3035
VBG           1458
VBN           2145
VBP           1318
VBZ           2124
WDT            440
WP             241
WP$             14
WRB            178
``             712
=======  =========

If you want to analyze the coverage of your own pickled tagger, use <span class="pre">--tagger</span> PATH/TO/TAGGER.pickle. You can also get detailed metrics on Found vs Actual counts, as well as Precision and Recall for each tag by using the <span class="pre">--metrics</span> argument with a corpus that provides a tagged_sents method, like treebank:

$ python analyze_tagger_coverage.py treebank --metrics
loading tagger taggers/maxent_treebank_pos_tagger/english.pickle
analyzing tag coverage of treebank with ClassifierBasedPOSTagger

Accuracy: 0.995689
Unknown words: 440

  Tag      Found      Actual      Precision      Recall
=======  =========  ==========  =============  ==========
#               16          16  1.0            1.0
$              724         724  1.0            1.0
''             694         694  1.0            1.0
,             4887        4886  1.0            1.0
-LRB-          120         120  1.0            1.0
-NONE-        6591        6592  1.0            1.0
-RRB-          126         126  1.0            1.0
.             3874        3874  1.0            1.0
:              563         563  1.0            1.0
CC            2271        2265  1.0            1.0
CD            3547        3546  0.99895833333  0.99895833333
DT            8170        8165  1.0            1.0
EX              88          88  1.0            1.0
FW               4           4  1.0            1.0
IN            9880        9857  0.99130434782  0.95798319327
JJ            5803        5834  0.99134948096  0.97892938496
JJR            386         381  1.0            0.91489361702
JJS            185         182  0.96666666666  1.0
LS              12          13  1.0            0.85714285714
MD             927         927  1.0            1.0
NN           13166       13166  0.99166034874  0.98791540785
NNP           9427        9410  0.99477911646  0.99398073836
NNPS           246         244  0.99029126213  0.95327102803
NNS           6055        6047  0.99515235457  0.99722414989
PDT             21          27  1.0            0.66666666666
POS            824         824  1.0            1.0
PRP           1716        1716  1.0            1.0
PRP$           766         766  1.0            1.0
RB            2800        2822  0.99305555555  0.975
RBR            130         136  1.0            0.875
RBS             33          35  1.0            0.5
RP             213         216  1.0            1.0
SYM              1           1  1.0            1.0
TO            2180        2179  1.0            1.0
UH               3           3  1.0            1.0
VB            2562        2554  0.99142857142  1.0
VBD           3035        3043  0.990234375    0.98065764023
VBG           1458        1460  0.99650349650  0.99824868651
VBN           2145        2134  0.98852223816  0.99566473988
VBP           1318        1321  0.99305555555  0.98281786941
VBZ           2124        2125  0.99373040752  0.990625
WDT            440         445  1.0            0.83333333333
WP             241         241  1.0            1.0
WP$             14          14  1.0            1.0
WRB            178         178  1.0            1.0
``             712         712  1.0            1.0
=======  =========  ==========  =============  ==========

These additional metrics can be quite useful for identifying which tags a tagger has trouble with. Precision answers the question “for each word that was given this tag, was it correct?”, while Recall answers the question “for all words that should have gotten this tag, did they get it?”. If you look at PDT, you can see that Precision is 100%, but Recall is 66%, meaning that every word that was given the PDT tag was correct, but 6 out of the 27 words that should have gotten PDT were mistakenly given a different tag. Or if you look at JJS, you can see that Precision is 96.6% because it gave JJS to 3 words that should have gotten a different tag, while Recall is 100% because all words that should have gotten JJS got it.