This is a language, loosely based on language Apache UIMA RUTA, focused on writing manual language rules, which compiles into either spaCy compatible patterns, or pure regex. These patterns can be used for doing manual NER as well as used in other processes, like retokenizing and pure matching
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pip install rita-dsl
rules = """
cuts = {"fitted", "wide-cut"}
lengths = {"short", "long", "calf-length", "knee-length"}
fabric_types = {"soft", "airy", "crinkled"}
fabrics = {"velour", "chiffon", "knit", "woven", "stretch"}
{IN_LIST(cuts)?, IN_LIST(lengths), WORD("dress")}->MARK("DRESS_TYPE")
{IN_LIST(lengths), IN_LIST(cuts), WORD("dress")}->MARK("DRESS_TYPE")
{IN_LIST(fabric_types)?, IN_LIST(fabrics)}->MARK("DRESS_FABRIC")
"""import spacy
from rita.shortcuts import setup_spacy
nlp = spacy.load("en")
setup_spacy(nlp, rules_string=rules)And using it:
>>> r = nlp("She was wearing a short wide-cut dress")
>>> [{"label": e.label_, "text": e.text} for e in r.ents]
[{'label': 'DRESS_TYPE', 'text': 'short wide-cut dress'}]import rita
patterns = rita.compile_string(rules, use_engine="standalone")And using it:
>>> list(patterns.execute("She was wearing a short wide-cut dress"))
[{'end': 38, 'label': 'DRESS_TYPE', 'start': 18, 'text': 'short wide-cut dress'}]