konsepy 0.3.2

Framework for build NLP information extraction systems using regular expressions.

konsepy

Framework for build NLP information extraction systems using regular expressions. konsepy then enables leveraging the NLP system to create a silver standard for fine-tuning a transformer model.

Installation

• konsepy is designed to be used with the knosepy_nlp_template
  • See the README there for current installation instructions.
• To use konsepy as a standalone entity:
  • Install with pip:
    • pip install konsepy[all]
    • For sentence-splitting corpora from fine-tuning a sentence based transformer, spacy will also need to be installed and configured.

Usage

The package provides a centralized CLI tool konsepy.

Building your NLP Package

To use konsepy, you need to create an NLP package (e.g., my_nlp_package) with the following structure. The best way to get this format is to clone the konsepy_nlp_template:

my_nlp_package/
├── __init__.py
└── concepts/
    ├── __init__.py
    └── my_concept.py

Each concept file (e.g., my_concept.py) must define:

• REGEXES: A list of regex-category pairs (and optional context functions).
• RUN_REGEXES_FUNC: A function that executes the regexes and returns categories/matches.
• CategoryEnum: An Enum defining the possible categories for the concept.

Search Functions

konsepy provides several pre-built search functions in konsepy.rxsearch:

Some simple ones:

• search_all_regex: Finds all occurrences of each regex in the list.
• search_first_regex: Finds only the first occurrence of each regex.

Probably the most useful:

• search_and_replace_regex_func: Prevents double-matching by replacing found text with dots before proceeding to the next regex.
• search_all_regex_func: Supports "sentinel" values (None) to stop processing if a match was found earlier.

Example of my_concept.py:

import re
from enum import Enum
from konsepy.rxsearch import search_all_regex_func
from konsepy.context.negation import check_if_negated
from konsepy.context.other_subject import check_if_other_subject


class CategoryEnum(Enum):
  MENTION = 1
  NO = 0
  OTHER = 3


REGEXES = [
  (re.compile(r'my pattern', re.I),
   CategoryEnum.MENTION,
   [
     lambda **kwargs: check_if_negated(neg_concept=CategoryEnum.NO, **kwargs),
     lambda **kwargs: check_if_other_subject(other_concept=CategoryEnum.OTHER, **kwargs),
   ]
   ),
]

# word_window specifies the number of words to retrieve for context functions (instead of character):
RUN_REGEXES_FUNC = search_all_regex_func(REGEXES, word_window=5)
# to alter the character-based window:
RUN_REGEXES_FUNC = search_all_regex_func(REGEXES, window=50)  # defaults to 30

Custom Search Functions

You can create your own search function by defining a function that returns a generator:

def my_custom_search(regexes):
    def _search(text, include_match=False):
        for regex, category, *other in regexes:
            for m in regex.finditer(text):
                yield (category, m) if include_match else category
    return _search

Running konsepy

# Run all concepts in a package against input files
konsepy run-all --package-name my_nlp_package --input-files data.csv --outdir output/

# Extract snippets for manual review
konsepy run4snippets --package-name my_nlp_package --input-files data.csv --outdir snippets/

# Generate BIO tagged data for model training
konsepy bio-tag --package-name my_nlp_package --input-files data.csv --outdir bio_data/

For more detailed documentation and a template, see konsepy_nlp_template.

Roadmap

• Change labels to some metadata object to allow more diverse input sources and run info