corenlp_ner.py

# get the named entity tag
    print('---')
    print('named entity tag of token')
    print(token.ner)
    # get an entity mention from the first sentence
    print('---')
    print('first entity mention in sentence')
    print(sentence.mentions[0])
    # access the coref chain
    print('---')
    print('coref chains for the example')
    print(ann.corefChain)
    # Use tokensregex patterns to find who wrote a sentence.
    pattern = '([ner: PERSON]+) /wrote/ /an?/ []{0,3} /sentence|article/'
    matches = client.tokensregex(text, pattern)
    # sentences contains a list with matches for each sentence.
    assert len(matches["sentences"]) == 3
    # length tells you whether or not there are any matches in this
    assert matches["sentences"][1]["length"] == 1
    # You can access matches like most regex groups.
    matches["sentences"][1]["0"]["text"] == "Chris wrote a simple sentence"
    matches["sentences"][1]["0"]["1"]["text"] == "Chris"
    # Use semgrex patterns to directly find who wrote what.
    pattern = '{word:wrote} >nsubj {}=subject >dobj {}=object'
    matches = client.semgrex(text, pattern)
    # sentences contains a list with matches for each sentence.
    assert len(matches["sentences"]) == 3
    # length tells you whether or not there are any matches in this
    assert matches["sentences"][1]["length"] == 1
    # You can access matches like most regex groups.
    matches["sentences"][1]["0"]["text"] == "wrote"
    matches["sentences"][1]["0"]["$subject"]["text"] == "Chris"
    matches["sentences"][1]["0"]["$object"]["text"] == "sentence"