https://github.com/wjbmattingly/spacyex

SpaCyEx allows the creation of spaCy Matcher patterns with RegEx like syntax.
https://github.com/wjbmattingly/spacyex

nlp spacy

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SpaCyEx allows the creation of spaCy Matcher patterns with RegEx like syntax.

• Host: GitHub
• URL: https://github.com/wjbmattingly/spacyex
• Owner: wjbmattingly
• Created: 2024-05-01T19:23:57.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-03T09:42:43.000Z (8 months ago)
• Last Synced: 2024-10-21T04:43:58.100Z (2 months ago)
• Topics: nlp, spacy
• Language: Python
• Homepage:
• Size: 58.6 KB
• Stars: 57
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# spaCyEx

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![logo](https://github.com/wjbmattingly/spacyex/blob/main/images/spacyex-logo.png?raw=true)

`spaCyEx` is a powerful extension for spaCy, designed to make pattern matching as flexible and easy as using regular expressions. It builds upon the existing capabilities of spaCy's `Matcher`, enhancing it with a more accessible syntax for defining complex patterns. `spaCyEx` allows for intuitive and detailed text pattern specifications, perfect for extracting detailed linguistic features from texts.

## Installation

You can install `spaCyEx` via pip:

```bash

pip install spacyex

```

## Features

- **Dynamic Pattern Creation**: Create complex token matching patterns using a simple string-based syntax.

- **Integration with spaCy**: Leverage spaCy's Matcher capabilities to find sequences in text that match defined patterns.

- **Customizable Matching Rules**: Define token attributes including text characteristics, lexical attributes, and grammatical properties.

### Creating Patterns

Define patterns using a string syntax where each token and its attributes are encapsulated by parentheses. Token attributes are specified by key-value pairs, separated by an equals sign (`=`), and multiple attributes are divided by a pipe (`|`).

#### Syntax Examples

- **Single Attribute**: `(pos=NOUN)`

- **Multiple Attributes**: `(pos=NOUN|lemma=run)`

- **Using List Values**: `(lemma=in[run,walk])`

- **Using Operators**: `(ent_type=person|op={2,3})`

### Pattern Matching

Once a pattern is defined, it can be used to search text for matches.

## Usage

Here is a simple example to get started with `spaCyEx`:

```python

import spacyex as se

import spacy

nlp = spacy.load("en_core_web_sm")

text = "John Smith runs fast, but Jacob Smith walks slowly."

pattern = "(ent_type=person|op={2}) (lemma=in[run,walk]) (pos=ADV)"

results = se.search(pattern, text, nlp)

for match in results:

    print(match[0].text, "Start:", match[1], "End:", match[2])

```

This code will match sequences in the text based on the defined pattern, using named entities, lemmas, and parts of speech.

## Roadmap

- [ ] Support for all dictionary properties in patterns.

- [ ] Additional utilities and helper functions for more complex pattern scenarios.