streamcorpus_pipeline/_taggers.py
def names_in_chains(stream_item, aligner_data):
    '''
    Convert doc-level Rating object into a Label, and add that Label
    to all Token in all coref chains identified by
    aligner_data["chain_selector"]

    :param stream_item: document that has a doc-level Rating to translate into token-level Labels.
    :param aligner_data: dict containing:
      chain_selector: ALL or ANY
      annotator_id: string to find at stream_item.Ratings[i].annotator.annotator_id

    If chain_selector==ALL, then only apply Label to chains in which
    all of the Rating.mentions strings appear as substrings within at
    least one of the Token.token strings.

    If chain_selector==ANY, then apply Label to chains in which any of
    the Rating.mentions strings appear as a substring within at least
    one of the Token.token strings.

    If chain_selector==ANY_MULTI_TOKEN, then apply Label to chains in which all
    the names in any of the Rating.mentions strings appear as a substring within at least
    one of the Token.token strings.
    '''
    chain_selector = aligner_data.get('chain_selector', '')
    assert chain_selector in _CHAIN_SELECTORS, \
        'chain_selector: %r not in %r' % (chain_selector, _CHAIN_SELECTORS.keys())

    ## convert chain_selector to a function
    chain_selector = _CHAIN_SELECTORS[chain_selector]

    ## make inverted index equiv_id --> (names, tokens)
    equiv_ids = make_chains_with_names( stream_item.body.sentences )

    required_annotator_id = aligner_data.get('annotator_id')

    for annotator_id, ratings in stream_item.ratings.items():
        if (required_annotator_id is not None) and (annotator_id != required_annotator_id):
            continue
        else:
            for rating in ratings:
                label = Label(annotator=rating.annotator,
                              target=rating.target)

                for eqid, (chain_mentions, chain_tokens) in equiv_ids.items():
                    if chain_selector(rating.mentions, chain_mentions):
                        ## apply the label
                        for tok in chain_tokens:
                            add_annotation(tok, label)
def names_in_chains(stream_item, aligner_data):
    '''
    Convert doc-level Rating object into a Label, and add that Label
    to all Token in all coref chains identified by
    aligner_data["chain_selector"]

    :param stream_item: document that has a doc-level Rating to translate into token-level Labels.
    :param aligner_data: dict containing:
      chain_selector: ALL or ANY
      annotator_id: string to find at stream_item.Ratings[i].annotator.annotator_id

    If chain_selector==ALL, then only apply Label to chains in which
    all of the Rating.mentions strings appear as substrings within at
    least one of the Token.token strings.

    If chain_selector==ANY, then apply Label to chains in which any of
    the Rating.mentions strings appear as a substring within at least
    one of the Token.token strings.

    If chain_selector==ANY_MULTI_TOKEN, then apply Label to chains in which all
    the names in any of the Rating.mentions strings appear as a substring within at least
    one of the Token.token strings.
    '''
    chain_selector = aligner_data.get('chain_selector', '')
    assert chain_selector in _CHAIN_SELECTORS, \
        'chain_selector: %r not in %r' % (chain_selector, _CHAIN_SELECTORS.keys())

    ## convert chain_selector to a function
    chain_selector = _CHAIN_SELECTORS[chain_selector]

    ## make inverted index equiv_id --> (names, tokens)
    equiv_ids = make_chains_with_names( stream_item.body.sentences )

    required_annotator_id = aligner_data.get('annotator_id')

    for annotator_id, ratings in stream_item.ratings.items():
        if (required_annotator_id is not None) and (annotator_id != required_annotator_id):
            continue
        else:
            for rating in ratings:
                label = Label(annotator=rating.annotator,
                              target=rating.target)

                for eqid, (chain_mentions, chain_tokens) in equiv_ids.items():
                    if chain_selector(rating.mentions, chain_mentions):
                        ## apply the label
                        for tok in chain_tokens:
                            add_annotation(tok, label)
def names_in_chains ( stream_item , aligner_data ) : chain_selector = aligner_data . get ( 'chain_selector' , '' ) assert chain_selector in _CHAIN_SELECTORS , 'chain_selector: %r not in %r' % ( chain_selector , _CHAIN_SELECTORS . keys ( ) ) ## convert chain_selector to a function chain_selector = _CHAIN_SELECTORS [ chain_selector ] ## make inverted index equiv_id --> (names, tokens) equiv_ids = make_chains_with_names ( stream_item . body . sentences ) required_annotator_id = aligner_data . get ( 'annotator_id' ) for annotator_id , ratings in stream_item . ratings . items ( ) : if ( required_annotator_id is not None ) and ( annotator_id != required_annotator_id ) : continue else : for rating in ratings : label = Label ( annotator = rating . annotator , target = rating . target ) for eqid , ( chain_mentions , chain_tokens ) in equiv_ids . items ( ) : if chain_selector ( rating . mentions , chain_mentions ) : ## apply the label for tok in chain_tokens : add_annotation ( tok , label )
Convert doc-level Rating object into a Label, and add that Label
    to all Token in all coref chains identified by
    aligner_data["chain_selector"]

    :param stream_item: document that has a doc-level Rating to translate into token-level Labels.
    :param aligner_data: dict containing:
      chain_selector: ALL or ANY
      annotator_id: string to find at stream_item.Ratings[i].annotator.annotator_id

    If chain_selector==ALL, then only apply Label to chains in which
    all of the Rating.mentions strings appear as substrings within at
    least one of the Token.token strings.

    If chain_selector==ANY, then apply Label to chains in which any of
    the Rating.mentions strings appear as a substring within at least
    one of the Token.token strings.

    If chain_selector==ANY_MULTI_TOKEN, then apply Label to chains in which all
    the names in any of the Rating.mentions strings appear as a substring within at least
    one of the Token.token strings.
Convert doc - level Rating object into a Label and add that Label to all Token in all coref chains identified by aligner_data [ chain_selector ]