streamcorpus_pipeline/_taggers.py
def multi_token_match(stream_item, aligner_data):
    '''
    iterate through tokens looking for near-exact matches to strings
    in si.ratings...mentions
    '''    
    tagger_id = _get_tagger_id(stream_item, aligner_data)
    sentences = stream_item.body.sentences.get(tagger_id)
    if not sentences:
        return
    ## construct a list of tuples, where the first part of each tuple
    ## is a tuple of cleansed strings, and the second part is the
    ## Token object from which it came.
    tokens = map(lambda tok: (cleanse(tok.token.decode('utf8')).split(' '), tok), 
                 itertools.chain(*[sent.tokens for sent in sentences]))    
    required_annotator_id = aligner_data['annotator_id']
    for annotator_id, ratings in stream_item.ratings.items():
        if (required_annotator_id is None) or (annotator_id == required_annotator_id):
            for rating in ratings:
                label = Label(annotator=rating.annotator,
                              target=rating.target)
                
                num_tokens_matched = 0
                for tok in look_ahead_match(rating, tokens):
                    if aligner_data.get('update_labels'):
                        tok.labels.pop(annotator_id, None)
                    add_annotation(tok, label)
                    num_tokens_matched += 1

                if num_tokens_matched == 0:
                    logger.warning('multi_token_match didn\'t actually match '
                                   'entity %r in stream_id %r',
                                   rating.target.target_id,
                                   stream_item.stream_id)
                else:
                    logger.debug('matched %d tokens for %r in %r',
                                 num_tokens_matched, rating.target.target_id,
                                 stream_item.stream_id)
def multi_token_match(stream_item, aligner_data):
    '''
    iterate through tokens looking for near-exact matches to strings
    in si.ratings...mentions
    '''    
    tagger_id = _get_tagger_id(stream_item, aligner_data)
    sentences = stream_item.body.sentences.get(tagger_id)
    if not sentences:
        return
    ## construct a list of tuples, where the first part of each tuple
    ## is a tuple of cleansed strings, and the second part is the
    ## Token object from which it came.
    tokens = map(lambda tok: (cleanse(tok.token.decode('utf8')).split(' '), tok), 
                 itertools.chain(*[sent.tokens for sent in sentences]))    
    required_annotator_id = aligner_data['annotator_id']
    for annotator_id, ratings in stream_item.ratings.items():
        if (required_annotator_id is None) or (annotator_id == required_annotator_id):
            for rating in ratings:
                label = Label(annotator=rating.annotator,
                              target=rating.target)
                
                num_tokens_matched = 0
                for tok in look_ahead_match(rating, tokens):
                    if aligner_data.get('update_labels'):
                        tok.labels.pop(annotator_id, None)
                    add_annotation(tok, label)
                    num_tokens_matched += 1

                if num_tokens_matched == 0:
                    logger.warning('multi_token_match didn\'t actually match '
                                   'entity %r in stream_id %r',
                                   rating.target.target_id,
                                   stream_item.stream_id)
                else:
                    logger.debug('matched %d tokens for %r in %r',
                                 num_tokens_matched, rating.target.target_id,
                                 stream_item.stream_id)
iterate through tokens looking for near-exact matches to strings
    in si.ratings...mentions