This demonstrates dynamic programming applied to optimally segmenting a contiguous string into words. The default language model is the ranked frequency distribution of unigrams from (English) Wikipedia, each with a frequency >= 200.
From this, a probability distribution or a Zipf rank-frequency distribution is defined over the ranked unigrams. The objective is to maximize the probability / Zipf of a particular segmentation.
Essentially, this is the Viterbi method applied to a unigram language model. For an interesting treatment, see the article by Peter Novig.
The user can also supply their own gzip'd (UTF-8 encoded) JSON file of word-frequency pairs, sorted descending by frequency.
See example.py for example usage:
from seg_str.segment import Segment
seg = Segment(cost_type="prob", word_freq_file=None) # defaults are shown
seg("mylifeboatisfullofeels")
# returns ['my', 'lifeboat', 'is', 'full', 'of', 'eels'], 51.29MIT License, copyright © 2021 Chris Skiscim
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