Rakuten MA Python

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Rakuten MA Python (morphological analyzer) is a Python version of Rakuten MA (word segmentor + PoS Tagger) for Chinese and Japanese.

For details about Rakuten MA, See https://github.com/rakuten-nlp/rakutenma

See also http://qiita.com/yukinoi/items/925bc238185aa2fad8a7 (In Japanese)

Contributions are welcome!

Installation

::

pip install rakutenma

Example

.. code:: python

from rakutenma import RakutenMA

Initialize a RakutenMA instance with an empty model

the default ja feature set is set already

rma = RakutenMA()

Let's analyze a sample sentence (from http://tatoeba.org/jpn/sentences/show/103809)

With a disastrous result, since the model is empty!

print(rma.tokenize("彼は新しい仕事できっと成功するだろう。"))

Feed the model with ten sample sentences from tatoeba.com

"tatoeba.json" is available at https://github.com/rakuten-nlp/rakutenma

import json tatoeba = json.load(open("tatoeba.json")) for i in tatoeba: rma.train_one(i)

Now what does the result look like?

print(rma.tokenize("彼は新しい仕事できっと成功するだろう。"))

Initialize a RakutenMA instance with a pre-trained model

rma = RakutenMA(phi=1024, c=0.007812) # Specify hyperparameter for SCW (for demonstration purpose) rma.load("model_ja.json")

Set the feature hash function (15bit)

rma.hash_func = rma.create_hash_func(15)

Tokenize one sample sentence

print(rma.tokenize("うらにわにはにわにわとりがいる"));

Re-train the model feeding the right answer (pairs of [token, PoS tag])

res = rma.train_one( [["うらにわ","N-nc"], ["に","P-k"], ["は","P-rj"], ["にわ","N-n"], ["にわとり","N-nc"], ["が","P-k"], ["いる","V-c"]])

The result of train_one contains:

sys: the system output (using the current model)

ans: answer fed by the user

update: whether the model was updated

print(res)

Now what does the result look like?

print(rma.tokenize("うらにわにはにわにわとりがいる"))

NOTE

Added API

As compared to original RakutenMA, following methods are added:

• RakutenMA::load(model_path)

  • Load model from JSON file

• RakutenMA::save(model_path)

  • Save model to path

misc

As initial setting, following values are set:

• rma.featset = CTYPE_JA_PATTERNS # RakutenMA.default_featset_ja
• rma.hash_func = rma.create_hash_func(15)
• rma.tag_scheme = "SBIEO" # if using Chinese, set "IOB2"

LICENSE

Apache License version 2.0

Copyright

Rakuten MA Python (c) 2015- Yukino Ikegami. All Rights Reserved.

Rakuten MA (original) (c) 2014 Rakuten NLP Project. All Rights Reserved.

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