How could spacy tokenize hashtag as a whole?

问题:

In a sentence containing hashtags, such as a tweet, spacy's tokenizer splits hashtags into two tokens:

import spacy
nlp = spacy.load('en')
doc = nlp(u'This is a #sentence.')
[t for t in doc]

output:

[This, is, a, #, sentence, .]

I'd like to have hashtags tokenized as such:

[This, is, a, #sentence, .]

Is that possible?

Thanks

回答1:

1. You can do some pre and post string manipulations,which shall make you bypass '#' based tokenization, and is easy to implement. e.g

> >>> import re
> >>> import spacy
> >>> nlp = spacy.load('en')
> >>> sentence = u'This is my twitter update #MyTopic'
> >>> parsed = nlp(sentence)
> >>> [token.text for token in parsed]

 [u'This', u'is', u'my', u'twitter', u'update', u'#', u'MyTopic']

> >>> new_sentence = re.sub(r'#(\w+)',r'ZZZPLACEHOLDERZZZ\1',sentence) 
> >>> new_sentence u'This is my twitter update ZZZPLACEHOLDERZZZMyTopic'
> >>> parsed = nlp(new_sentence)
> >>> [token.text for token in parsed]

 [u'This', u'is', u'my', u'twitter', u'update', u'ZZZPLACEHOLDERZZZMyTopic']

> >>> [x.replace(u'ZZZPLACEHOLDERZZZ','#') for x in [token.text for token in parsed]]

 [u'This', u'is', u'my', u'twitter', u'update', u'#MyTopic']

2. You can try setting custom seperators in spacy's tokenizer. I am not aware of methods to do that.

UPDATE : You can use a regex to find span of token you would want to stay as single token, and retokenize using span.merge method as mentioned here : https://spacy.io/docs/api/span#merge

Merge example:

>>> import spacy
>>> import re
>>> nlp = spacy.load('en')
>>> my_str = u'Tweet hashtags #MyHashOne #MyHashTwo'
>>> parsed = nlp(my_str)
>>> [(x.text,x.pos_) for x in parsed]
[(u'Tweet', u'PROPN'), (u'hashtags', u'NOUN'), (u'#', u'NOUN'), (u'MyHashOne', u'NOUN'), (u'#', u'NOUN'), (u'MyHashTwo', u'PROPN')]
>>> indexes = [m.span() for m in re.finditer('#\w+',my_str,flags=re.IGNORECASE)]
>>> indexes
[(15, 25), (26, 36)]
>>> for start,end in indexes:
...     parsed.merge(start_idx=start,end_idx=end)
... 
#MyHashOne
#MyHashTwo
>>> [(x.text,x.pos_) for x in parsed]
[(u'Tweet', u'PROPN'), (u'hashtags', u'NOUN'), (u'#MyHashOne', u'NOUN'), (u'#MyHashTwo', u'PROPN')]
>>> 

回答2:

This is more of a add-on to the great answer by @DhruvPathak AND a shameless copy from the below linked github thread (and the even better answer by @csvance). spaCy features (since V2.0) the add_pipe method. Meaning you can define @DhruvPathak great answer in a function and add the step (conveniently) into your nlp processing pipeline, as below.

Citations starts here:

def hashtag_pipe(doc):
    merged_hashtag = False
    while True:
        for token_index,token in enumerate(doc):
            if token.text == '#':
                if token.head is not None:
                    start_index = token.idx
                    end_index = start_index + len(token.head.text) + 1
                    if doc.merge(start_index, end_index) is not None:
                        merged_hashtag = True
                        break
        if not merged_hashtag:
            break
        merged_hashtag = False
    return doc

nlp = spacy.load('en')
nlp.add_pipe(hashtag_pipe)

doc = nlp("twitter #hashtag")
assert len(doc) == 2
assert doc[0].text == 'twitter'
assert doc[1].text == '#hashtag'

Citation ends here; Check out how to add hashtags to the part of speech tagger #503 for the full thread.

PS It's clear when reading the code, but for the copy&pasters, don't disable the parser :)

回答3:

I found this on github, which uses spaCy's Matcher:

from spacy.matcher import Matcher

matcher = Matcher(nlp.vocab)
matcher.add('HASHTAG', None, [{'ORTH': '#'}, {'IS_ASCII': True}])

doc = nlp('This is a #sentence. Here is another #hashtag. #The #End.')
matches = matcher(doc)
hashtags = []
for match_id, start, end in matches:
    hashtags.append(doc[start:end])

for span in hashtags:
    span.merge()

print([t.text for t in doc])

outputs:

['This', 'is', 'a', '#sentence', '.', 'Here', 'is', 'another', '#hashtag', '.', '#The', '#End', '.']

A list of hashtags is also available in the hashtags list:

print(hashtags)

output:

[#sentence, #hashtag, #The, #End]

回答4:

I spent quite a bit of time on this and found I share what I came up with: Subclassing the Tokenizer and adding the regex for hashtags to the default URL_PATTERN was the easiest solution for me, additionally adding a custom extension to match on hashtags to identify them:

import re
import spacy
from spacy.language import Language
from spacy.tokenizer import Tokenizer
from spacy.tokens import Token

nlp = spacy.load('en_core_web_sm')

def create_tokenizer(nlp):
    # contains the regex to match all sorts of urls:
    from spacy.lang.tokenizer_exceptions import URL_PATTERN

    # spacy defaults: when the standard behaviour is required, they
    # need to be included when subclassing the tokenizer
    prefix_re = spacy.util.compile_prefix_regex(Language.Defaults.prefixes)
    infix_re = spacy.util.compile_infix_regex(Language.Defaults.infixes)
    suffix_re = spacy.util.compile_suffix_regex(Language.Defaults.suffixes)

    # extending the default url regex with regex for hashtags with "or" = |
    hashtag_pattern = r'''|^(#[\w_-]+)$'''
    url_and_hashtag = URL_PATTERN + hashtag_pattern
    url_and_hashtag_re = re.compile(url_and_hashtag)

    # set a custom extension to match if token is a hashtag
    hashtag_getter = lambda token: token.text.startswith('#')
    Token.set_extension('is_hashtag', getter=hashtag_getter)

    return Tokenizer(nlp.vocab, prefix_search=prefix_re.search,
                     suffix_search=suffix_re.search,
                     infix_finditer=infix_re.finditer,
                     token_match=url_and_hashtag_re.match
                     )

nlp.tokenizer = create_tokenizer(nlp)
doc = nlp("#spreadhappiness #smilemore [e-mail] https://www.somedomain.com/foo")

for token in doc:
    print(token.text)
    if token._.is_hashtag:
        print("-> matches hashtag")

# returns: "#spreadhappiness -> matches hashtag #smilemore -> matches hashtag [e-mail] https://www.somedomain.com/foo"