Patterns

Patterns let you find documents, sentences or tokens according to certain rules that will describe token attributes and relations between tokens. Attributes refer to information about a token like postag, raw text, dependency, entity type ... Patterns follow a dictionary structure and can be loaded from json files.

Token patterns are simple patterns that do not use dependency parsing. They consist of a sequence of attributes related rules.

Attributes are the properties of a token after analysis by the comprehension API. By defining an attribute in a pattern, only tokens that match the specific attribute will be matched.

Each attribute can map either to a single value or to a dictionary that allows modifiers for more complex behaviors.

Attribute

Operators work similarly as regular expressions operators, they allow to choose how often should a token be matched.

patterns = {
    "patients":
        [
            {"POS":{"IN":["CD", "ENTITY"]}},
            {"POS":{"IN":["RB", "JJ", "PUNCT", "ENTITY"]}, "OP":"*"},
            {"LEMMA":"patient"}
        ],
    "date":
        [
            {"ENT_TYPE":"duration"}
        ]
}

text = "3416 consecutive patients were reviewed and 1476 finally enrolled (65.9 ± 20.9 years, 57.3% male). 76 (5.1%) patients had NAEs. Of 444 patients, 76% were male. They had a mean age of 69 ± 10 years."
nlp.add_document(text)

for s, matches in nlp.match_pattern(patterns, level='sentence'):
    print(s.str)
    print(matches)

Dependency patterns use dependency parsing which construct a grammatical tree of the sentence to allow complex matching patterns. Attributes matching is similar to Token Patterns (Only exception is that for operators only "?" is available) but there are also relation operators specific to dependency matching. Matching between the pattern and the sentence does not use the order of token (like for Token Patterns) but the dependency relations between tokens.

A Dependency Pattern consist of a list of dictionary formated in this way:

All fields must be completed except for the root node which only needs 'RIGHT_ID' and 'RIGHT_ATTRS' fields. Each pattern must have one root node.

Dependency matching should not be used on Subsentence since they don't have a complete dependency tree.

nlp.add_document("Megasoft sues PearCorp for copyright infringement.")
pattern = {
  'pattern1':[
    {
      "RIGHT_ID": "verb",
      "RIGHT_ATTRS": {"POS": "V", "ORTH":" "}
    },
    {
      "LEFT_ID": "verb",
      "REL_OP": ">",
      "RIGHT_ID": "subject",
      "RIGHT_ATTRS": {"DEP": "nsubj"}
    },
    {
      "LEFT_ID": "verb",
      "REL_OP": ">",
      "RIGHT_ID": "object",
      "RIGHT_ATTRS": {"DEP": "obj"}
    }
  ]
}

for document, match_results in nlp.match_pattern(pattern, level='document'): 
  print(document)
  for pattern, matches in match_results.items():
    print(pattern, matches)

[Megasoft sues PearCorp for copyright infringement.]  
pattern1 [[sues, Megasoft, PearCorp]]