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exclude 'extenders' from the start of the token

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Former-commit-id: a8e7c29068
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Robyn Speer 2015-08-25 12:33:12 -04:00
parent 4801b0d876
commit 0b282c5055
1 changed files with 23 additions and 14 deletions
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37
wordfreq/tokens.py
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@ -5,30 +5,39 @@ import unicodedata
# Here's what the following regular expression is looking for:
#
# At the start, it looks for a character in the set \S -- the set of
# non-punctuation -- with various characters subtracted out, including punctuation
# and most of the 'symbol' categories. (We leave So, "Symbol - Other", because
# it contains things like emoji that have interesting frequencies. This is why
# we don't just insist on the token starting with a "word" character, \w.)
# non-punctuation -- with various characters subtracted out, including
# punctuation and most of the 'symbol' categories. (We leave So, "Symbol -
# Other", because it contains things like emoji that have interesting
# frequencies. This is why we don't just insist on the token starting with a
# "word" character, \w.)
#
# After it has found one such character, the rest of the token is (?:\B\S)*,
# which continues to consume characters as long as the next character does not
# cause a word break (\B) and is not a space (\S). The individual characters in
# this portion can be punctuation, allowing tokens such as "can't" or
# "google.com".
# WB=Extend is a Unicode property that says, for the purpose of word breaking,
# that this character should get the word-breaking properties of the previous
# character. It's used for combining marks and stuff. If it shows up at the
# beginning of the token, something has gone wrong, so exclude it as a token.
#
# After it has found a starting character, the rest of the token matches
# (?:\B\S)*, which continues to consume characters as long as the next
# character does not cause a word break (\B) and is not a space (\S). The
# individual characters in this portion can be punctuation, allowing tokens
# such as "can't" or "google.com".
#
# As a complication, the rest of the token can match a glob of Han ideographs
# (\p{IsIdeo}) and hiragana (\p{Script=Hiragana}). Chinese words are made of
# Han ideographs (but we don't know how many). Japanese words are either made
# of Han ideographs and hiragana (which will be matched by this expression), or
# katakana (which will be matched by the standard Unicode rule).
# Han ideographs (but we don't know where the breaks between them are).
# Similarly, Japanese words are either made of Han ideographs and hiragana
# (which will be matched by this expression), or katakana (which will be
# matched by the standard Unicode rule).
#
# Without this special case for ideographs and hiragana, the standard Unicode
# rule would put each character in its own token. This actually would be the
# correct behavior for word-wrapping, but it's an ugly failure mode for NLP
# tokenization.
TOKEN_RE = regex.compile(r'[\S--[\p{punct}\p{Sm}\p{Sc}\p{Sk}]](?:\B\S|[\p{IsIdeo}\p{Script=Hiragana}])*', regex.V1 | regex.WORD)
ARABIC_MARK_RE = regex.compile(r'[[\p{Mn}&&\p{Block=Arabic}]\N{ARABIC TATWEEL}]', regex.V1)
TOKEN_RE = regex.compile(
r'[\S--[\p{punct}\p{Sm}\p{Sc}\p{Sk}\p{WB=Extend}]]'
r'(?:\B\S|[\p{IsIdeo}\p{Script=Hiragana}])*', regex.V1 | regex.WORD)
ARABIC_MARK_RE = regex.compile(r'[\p{Mn}\N{ARABIC TATWEEL}]', regex.V1)
def simple_tokenize(text):