streamcorpus_pipeline/_taggers.py
def make_chains_with_names(sentences):
    '''
    assemble in-doc coref chains by mapping equiv_id to tokens and
    their cleansed name strings

    :param sentences: iterator over token generators
    :returns dict:
        keys are equiv_ids,
        values are tuple(concatentated name string, list of tokens)
    '''
    ## if an equiv_id is -1, then the token is classified into some
    ## entity_type but has not other tokens in its chain.  We don't
    ## want these all lumped together, so we give them distinct "fake"
    ## equiv_id other than -1 -- counting negatively to avoid
    ## collisions with "real" equiv_ids
    fake_equiv_ids = -2

    ## use a default dictionary
    equiv_ids = collections.defaultdict(lambda: (set(), set()))

    for tagger_id, sents in sentences.items():
        for sent in sents:
            for tok in sent.tokens:
                if tok.entity_type is not None:

                    ## get an appropriate equiv_id
                    if tok.equiv_id == -1:
                        eqid = fake_equiv_ids
                        fake_equiv_ids -= 1
                    else:
                        eqid = tok.equiv_id

                    ## store the name parts initially as a set
                    equiv_ids[eqid][0].add(cleanse(tok.token.decode('utf8')))
                    ## carry a *reference* to the entire Token object
                    equiv_ids[eqid][1].add(tok)

    return equiv_ids
def make_chains_with_names(sentences):
    '''
    assemble in-doc coref chains by mapping equiv_id to tokens and
    their cleansed name strings

    :param sentences: iterator over token generators
    :returns dict:
        keys are equiv_ids,
        values are tuple(concatentated name string, list of tokens)
    '''
    ## if an equiv_id is -1, then the token is classified into some
    ## entity_type but has not other tokens in its chain.  We don't
    ## want these all lumped together, so we give them distinct "fake"
    ## equiv_id other than -1 -- counting negatively to avoid
    ## collisions with "real" equiv_ids
    fake_equiv_ids = -2

    ## use a default dictionary
    equiv_ids = collections.defaultdict(lambda: (set(), set()))

    for tagger_id, sents in sentences.items():
        for sent in sents:
            for tok in sent.tokens:
                if tok.entity_type is not None:

                    ## get an appropriate equiv_id
                    if tok.equiv_id == -1:
                        eqid = fake_equiv_ids
                        fake_equiv_ids -= 1
                    else:
                        eqid = tok.equiv_id

                    ## store the name parts initially as a set
                    equiv_ids[eqid][0].add(cleanse(tok.token.decode('utf8')))
                    ## carry a *reference* to the entire Token object
                    equiv_ids[eqid][1].add(tok)

    return equiv_ids
assemble in-doc coref chains by mapping equiv_id to tokens and
    their cleansed name strings

    :param sentences: iterator over token generators
    :returns dict:
        keys are equiv_ids,
        values are tuple(concatentated name string, list of tokens)