Using ARPA LM with Python

During Christmas, I tried to do some small fun hack with language modeling.  That obviously requires reading and evaluating an LM.   There are many ways to do so.   But here is new method which I really like: use the python interface of KenLM.

So here is a note for myself (largely adapted from Victor Chahuneau):

Install boost-1.6.0

To install KenLM, you need to first install boost.  If you want to install boost, then you need to install libbz2.

  1. First, install libbz2:
    sudo apt-get install libbz2-dev
  2. Then install 1.6.0:  download here, then type

    , and finally

    ./bj2 -j 4
  3. Install boost:
    ./bj2 install

Install KenLM

Now we install kenlm, I am using the copy of Victor's here.

git clone
pushd kenlm
python install

Training an LM

Download some books from Gutenberg, I am using Chambers's Journal of Popular Literature, Science, and Art, No. 723.  And I got this file,

So all you need to do to train a model is,

cat 50780-0.txt| /home/archan/src/github/kenlm/bin/lmplz -o 3 >

Then you can binarize the LM, which is the part I like about KenLM, it feels snappy and fast than other toolkits I used.

/home/archan/src/github/kenlm/bin/build_binary yourLM.klm

Evaluate a Sentence with an LM

Write a python script like this:

import kenlm
model = kenlm.LanguageModel('yourLM.klm')
score = model.score('i like science fiction')
print score


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