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English word segmentation.

Project Description

WordSegmentation is an Apache2 licensed module for English word segmentation, written in pure-Python, and based on a trillion-word corpus.

Inspired by Grant Jenks Based on word weighing algorithm from the chapter “Natural Language Corpus Data” by Peter Norvig from the book “Beautiful Data” (Segaran and Hammerbacher, 2009).

Data files are derived from the Google Web Trillion Word Corpus, as described by Thorsten Brants and Alex Franz, and distributed by the Linguistic Data Consortium.


  • Pure-Python
  • Segmentation Algorithm Using Divide and Conquer so that there is NO max length limit set to input text.
  • Segmentation Algotrithm used Dynamic Programming to achieve a polynomial time complexity.
  • Used Google Trillion Corpus to do scoring for the word segmentation.
  • Developed on Python 2.7
  • Tested on CPython 2.6, 2.7, 3.4.


Installing WordSegment is simple with pip:

$ pip install wordsegmentation

Dependency required networkx:

$ pip install networkx


In your own Python programs, you’ll mostly want to use segment to divide a phrase into a list of its parts:

>>> from wordsegmentation import WordSegment
>>> ws = WordSegment()

>>> ws.segment('universityofwashington')
['university', 'of', 'washington']
>>> ws.segment('thisisatest')
['this', 'is', 'a', 'test']
>>> ws.segment('thisisanURLcontaining123345and-&**^&butitstillworks')
['this', 'is', 'an', 'url', 'containing', '123345', 'and', '-&**^&', 'but', 'it', 'still', 'works']
>>> ws.segment('NoMatterHowLongThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextThisTextMightBe')
['no', 'matter', 'how', 'long', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'this', 'text', 'might', 'be']

Bug Report


weihan.github AT

Tech Details

In the code, the segmentation algorithm consists of the following steps,

  1. divide and conquer – safely divide the input string into substring. This way we solved the length limit which will dramatically slow down the performance. For example, “facebook123helloworld” will be treated as 3 sub-problems – “facebook”, “123”, and “helloworld”.
  2. for each sub-string. I used dynamic programming to calculate and get the optimal words.
  3. combine the sub-problems, and return the result for the original string.

Segmentation algorithm used in this module, has achieved a time-complexity of O(n^2). By comparison to existing segmentation algorithms, this module does better on following aspects,

  1. can handle very long input. There is no arbitary max lenght limit set to input string.
  2. segmentation finished in polynomial time via dynamic programming.
  3. by default, the algorithm uses a filtered Google corpus, which contains only English words that could be found in dictionary.
An extreme example is to solve the classic English Scriptio_continua segmentation problem as shown below::

Our algorithm solved this issue in polynomial time, and the output is:

[‘margaret', 'are', 'you', 'grieving', 'over', 'golden', 'grove', 'un', 'leaving', 'leaves', 'like', 'the', 'things', 'of', 'man', 'you', 'with', 'your', 'fresh', 'thoughts', 'care', 'for', 'can', 'you', 'a', 'has', 'the', 'he', 'art', 'grows', 'older', 'it', 'will', 'come', 'to', 'such', 'sights', 'colder', 'by', 'and', 'by', 'nor', 'spa', 're', 'a', 'sigh', 'though', 'worlds', 'of', 'wan', 'wood', 'leaf', 'me', 'allie', 'and', 'yet', 'you', 'will', 'weep', 'and', 'know', 'why', 'now', 'no', 'matter', 'child', 'the', 'name', 'sorrows', 'springs', 'are', 'the', 'same', 'nor', 'mouth', 'had', 'non', 'or', 'mind', 'expressed', 'what', 'he', 'art', 'heard', 'of', 'ghost', 'guessed', 'it', 'is', 'the', 'blight', 'man', 'was', 'born', 'for', 'it', 'is', 'margaret', 'you', 'mourn', 'for']
Release History

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wordsegmentation-0.3.5-py2.py3-none-any.whl (8.2 MB) Copy SHA256 Checksum SHA256 py2.py3 Wheel Oct 26, 2015
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