A package leveraging FFI (foreign function interface), natto-py
combines
the Python programming language with MeCab, the part-of-speech and
morphological analyzer for the Japanese language. No compiler is necessary, as
it is not a C extension. natto-py
will run on Mac OS, Windows and
*nix.
You can learn more about natto-py at GitHub.
natto-py
requires the following:
- An existing installation of MeCab 0.996
- A system dictionary, like IPA, Juman or Unidic
- cffi 0.8.6 or greater
The following Python versions are supported:
Install natto-py
as you would any other Python package:
$ pip install natto-py
This will automatically install the cffi
package, which natto-py
uses
to bind to the mecab
library.
As long as the mecab
(and mecab-config
for *nix and Mac OS)
executables are on your PATH
, natto-py
does not require any explicit
configuration.
- On *nix and Mac OS, it queries
mecab-config
to discover the path to thelibmecab.so
orlibmecab.dylib
, respectively. - On Windows, it queries the Windows Registry to locate the MeCab installation folder.
- In order to convert character encodings to/from Unicode,
natto-py
will examine the charset of themecab
system dictionary.
If natto-py
for some reason cannot locate the mecab
library,
or if it cannot determine the correct charset used internally by
mecab
, then you will need to set the MECAB_PATH
and MECAB_CHARSET
environment variables.
- Set the
MECAB_PATH
environment variable to the exact name/path to yourmecab
library. - Set the
MECAB_CHARSET
environment variable to thecharset
character encoding used by your system dictionary.
e.g., for Mac OS:
export MECAB_PATH=/usr/local/Cellar/mecab/0.996/lib/libmecab.dylib
export MECAB_CHARSET=utf8
e.g., for bash on UNIX/Linux:
export MECAB_PATH=/usr/local/lib/libmecab.so
export MECAB_CHARSET=euc-jp
e.g., on Windows:
set MECAB_PATH=C:\Program Files\MeCab\bin\libmecab.dll
set MECAB_CHARSET=shift-jis
e.g., from within a Python program:
import os
os.environ['MECAB_PATH']='/usr/local/lib/libmecab.so'
os.environ['MECAB_CHARSET']='utf-16'
Here's a very quick guide to using natto-py
.
Instantiate a reference to the mecab
library, and display some details:
from natto import MeCab
nm = MeCab()
print(nm)
# displays details about the MeCab instance
<natto.mecab.MeCab
model=<cdata 'mecab_model_t *' 0x801c16300>,
tagger=<cdata 'mecab_t *' 0x801c17470>,
lattice=<cdata 'mecab_lattice_t *' 0x801c196c0>,
libpath="/usr/local/lib/libmecab.so",
options={},
dicts=[<natto.dictionary.DictionaryInfo
dictionary='mecab_dictionary_info_t *' 0x801c19540>,
filepath="/usr/local/lib/mecab/dic/ipadic/sys.dic",
charset=utf8,
type=0],
version=0.996>
Display details about the mecab
system dictionary used:
sysdic = nm.dicts[0]
print(sysdic)
# displays the MeCab system dictionary info
<natto.dictionary.DictionaryInfo
dictionary='mecab_dictionary_info_t *' 0x801c19540>,
filepath="/usr/local/lib/mecab/dic/ipadic/sys.dic",
charset=utf8,
type=0>
Parse Japanese text and send the MeCab result as a single string to
stdout
:
print(nm.parse('ピンチの時には必ずヒーローが現れる。'))
# MeCab result as a single string
ピンチ 名詞,一般,*,*,*,*,ピンチ,ピンチ,ピンチ
の 助詞,連体化,*,*,*,*,の,ノ,ノ
時 名詞,非自立,副詞可能,*,*,*,時,トキ,トキ
に 助詞,格助詞,一般,*,*,*,に,ニ,ニ
は 助詞,係助詞,*,*,*,*,は,ハ,ワ
必ず 副詞,助詞類接続,*,*,*,*,必ず,カナラズ,カナラズ
ヒーロー 名詞,一般,*,*,*,*,ヒーロー,ヒーロー,ヒーロー
が 助詞,格助詞,一般,*,*,*,が,ガ,ガ
現れる 動詞,自立,*,*,一段,基本形,現れる,アラワレル,アラワレル
。 記号,句点,*,*,*,*,。,。,。
EOS
Next, try parsing the text with MeCab node parsing. A generator yielding the MeCabNode instances lets you efficiently iterate over the output without first materializing each and every resulting MeCabNode instance. The MeCabNode instances yielded allow access to more detailed information about each morpheme.
Here we use a Python with-statement to automatically clean up after we
finish node parsing with the MeCab tagger. This is the recommended approach
for using natto-py
in a production environment:
# Use a Python with-statement to ensure mecab_destroy is invoked
#
with MeCab() as nm:
for n in nm.parse('ピンチの時には必ずヒーローが現れる。', as_nodes=True):
... # ignore any end-of-sentence nodes
... if not n.is_eos():
... print('{}\t{}'.format(n.surface, n.cost))
...
ピンチ 3348
の 3722
時 5176
に 5083
は 5305
必ず 7525
ヒーロー 11363
が 10508
現れる 10841
。 7127
MeCab output formatting is extremely flexible and is highly recommended for
any serious natural language processing task. Rather than parsing the MeCab
output as a single, large string, use MeCab's --node-format
option
(short form -F
) to customize the node's feature
attribute.
- morpheme surface
- part-of-speech
- part-of-speech ID
- pronunciation
This example formats the node feature
to capture the items above as a
comma-separated value:
# MeCab options used:
#
# -F ... short-form of --node-format
# %m ... morpheme surface
# %f[0] ... part-of-speech
# %h ... part-of-speech id (ipadic)
# %f[8] ... pronunciation
#
with MeCab('-F%m,%f[0],%h,%f[8]') as nm:
for n in nm.parse('ピンチの時には必ずヒーローが現れる。', as_nodes=True):
... # only normal nodes, ignore any end-of-sentence and unknown nodes
... if n.is_nor():
... print(n.feature)
...
ピンチ,名詞,38,ピンチ
の,助詞,24,ノ
時,名詞,66,トキ
に,助詞,13,ニ
は,助詞,16,ワ
必ず,副詞,35,カナラズ
ヒーロー,名詞,38,ヒーロー
が,助詞,13,ガ
現れる,動詞,31,アラワレル
。,記号,7,。
Partial parsing (制約付き解析), allows you to pass hints to MeCab on how to tokenize morphemes when parsing. Most useful are boundary constraint parsing and feature constraint parsing.
With boundary constraint parsing, you can specify either a compiled re
regular expression object or a string to tell MeCab where the boundaries of
a morpheme should be. Use the boundary_constraints
keyword. For hints on
tokenization, please see Regular expression operations and re.finditer
in particular.
This example uses the -F
node-format option to customize the resulting
MeCabNode
feature attribute to extract:
%m
- morpheme surface%f[0]
- node part-of-speech%s
- nodestat
status value, 1 isunknown
Note that any such morphemes captured will have node stat
status of 1 (unknown):
import re
with MeCab('-F%m,\s%f[0],\s%s') as nm:
text = '俺は努力したよっ? お前の10倍、いや100倍1000倍したよっ!'
# capture 10倍, 100倍 and 1000倍 as single parts-of-speech
pattern = re.compile('10+倍')
for n in nm.parse(text, boundary_constraints=pattern, as_nodes=True):
... print(n.feature)
...
俺, 名詞, 0
は, 助詞, 0
努力, 名詞, 0
し, 動詞, 0
たよっ, 動詞, 0
?, 記号, 0
お前, 名詞, 0
の, 助詞, 0
10倍, 名詞, 1
、, 記号, 0
いや, 接続詞, 0
100倍, 名詞, 1
1000倍, 名詞, 1
し, 動詞, 0
たよっ, 動詞, 0
!, 記号, 0
EOS
With feature constraint parsing, you can provide instructions to MeCab
on what feature to use for a matching morpheme. Use the
feature_constraints
keyword to pass in a tuple
containing elements
that themselves are tuple
instances with a specific morpheme (str)
and a corresponding feature (str), in order of constraint precedence:
with MeCab('-F%m,\s%f[0],\s%s') as nm:
text = '心の中で3回唱え、 ヒーロー見参!ヒーロー見参!ヒーロー見参!'
features = (('ヒーロー見参', '感動詞'),)
for n in nm.parse(text, feature_constraints=features, as_nodes=True):
... print(n.feature)
...
心, 名詞, 0
の, 助詞, 0
中, 名詞, 0
で, 助詞, 0
3, 名詞, 1
回, 名詞, 0
唱え, 動詞, 0
、, 記号, 0
ヒーロー見参, 感動詞, 1
!, 記号, 0
ヒーロー見参, 感動詞, 1
!, 記号, 0
ヒーロー見参, 感動詞, 1
!, 記号, 0
EOS
- Examples and more detailed information about
natto-py
can be found on the project Wiki. - Working code in Jupyter notebook form can be found under this project's notebooks directory.
- API documentation on Read the Docs.
- Use git and check out the latest code at GitHub to make sure the feature hasn't been implemented or the bug hasn't been fixed yet.
- Browse the issue tracker to make sure someone already hasn't requested it and/or contributed it.
- Fork the project.
- Start a feature/bugfix branch.
- Commit and push until you are happy with your contribution.
- Make sure to add tests for it. This is important so I don't break it in a future version unintentionally.
- Please try not to mess with the
setup.py
,CHANGELOG
, or version files. If you must have your own version, that is fine, but please isolate to its own commit so I can cherry-pick around it. - This project uses the following packages for development:
Please see the CHANGELOG
for the release history.
Copyright © 2016, Brooke M. Fujita. All rights reserved. Please see
the LICENSE
file for further details.