spatgen 0.1.1

Pattern generating DSL for spaCy

Spatgen: Pattern generator for spaCy

Spatgen is a concise and readable DSL and parser which produces patterns for spaCy which you can use in the Matcher class.

We think Matcher is fantastic but writing patterns can become tedious especially if there are several combinations with the same prefix or suffix and if the rules don't fall into any of the OP operations provided. This package simplifies the creation of large lists of patterns.

Example

Here's an example showing how to match some syntax with optional and either-or patterns (you can combine these too):

from spatgen import parser

patterns = r"""
name = example
anchors = [study survey report research datum article review]

++match++
lemma(anchors) aux? adv? verb adp? <det? noun | date> [adp det? <org | gpe>]
"""

parser.parse_str(str(patterns))

This will produce a Sections object which contains a list of patterns (suitable for Matcher) for each heading found in the input. It also has a get_meta method which allows you to retrieve extra information about the pattern such as its line number and the raw source that led to its creation.

Syntax

Variables

Names can contain lowercase alphabetic characters and underscores only. Values are interpreted as either strings or arrays of strings and quotation marks are not required.

Sections

Sections are demarcated by surrounding an identifier with ++ on either side.

Patterns

Patterns loosely follow command-line syntax format.

• [a ...] means whatever inside is optional. A pattern will be generated for combinations with and without this option.
• <a | b | ...> means either a or b (or more). Patterns are generated for every combination.
• action(param) interprets action to be one of spaCy's token operators (LEMMA, LOWER, etc).
  • If param is a string surrounded by quotes then the pattern is compared directly (e.g. {"LEMMA": "say"}).
  • If param is an identifier and is specified as a variable then the pattern is an IN pattern ( e.g. {"LEMMA": {"IN": var}}).
• Other terms are interpreted as either POS tags or entity types. These are matched on context so the spaCy pattern will have the correct type.
  • E.g. verb will produce {"POS": "VERB"} but org will produce {"ENT_TYPE": "ORG"}.
• Every match supports modifiers and will produce the corresponding OP entry:
  • ? will match zero or one
  • * will match zero or more
  • + will match one or more
  • ! will ensure that none match