RITA DSL
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
An Introduction Video
Links
- Website
- Simple Chat bot example
- Documentation
- QuickStart
- Language Syntax Plugin for IntelijJ based IDEs
Support
If you need consulting or some custom work done, you can Contact Us
Install
pip install rita-dsl
Simple Rules example
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")
"""
Loading in spaCy
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'}]
Loading using Regex (standalone)
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'}]
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