https://github.com/rlayers/pawpaw
Text Processing & Segmentation Framework
https://github.com/rlayers/pawpaw
extract-text hierarchical-text-segmentation information-extraction knowledge-graph lexer natural-language-processing nlp parser python query-engine query-language text-processing text-segmentation tree xml-parser xmlparser
Last synced: 21 days ago
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Text Processing & Segmentation Framework
- Host: GitHub
- URL: https://github.com/rlayers/pawpaw
- Owner: rlayers
- License: mit
- Created: 2022-10-04T21:50:29.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2025-09-18T18:10:02.000Z (7 months ago)
- Last Synced: 2025-12-15T10:41:08.732Z (4 months ago)
- Topics: extract-text, hierarchical-text-segmentation, information-extraction, knowledge-graph, lexer, natural-language-processing, nlp, parser, python, query-engine, query-language, text-processing, text-segmentation, tree, xml-parser, xmlparser
- Language: Python
- Homepage:
- Size: 5.35 MB
- Stars: 25
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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# Pawpaw [![High Performance Text Segmentation, Parsing, & Query][byline-img]][repo]
Pawpaw is a high-performance framework for parsing and text segmentation. The segments it generates are automatically organized into tree graphs, which can be easily serialized, traversed, and queried using a powerful structured query language called *plumule*. Creating a tree graph is simple — just provide spans, gaps, substrings, or even a regex/match object. Additionally, Pawpaw includes a robust pipelining engine, enabling you to create complex, multi-step, rules-based text parsers.
- Indexed string and substring representation
- Efficient memory utilization
- Fast processing
- Pythonic relative indexing and slicing
- Runtime & polymorphic value extraction
- Tree graphs for all indexed text
- Search and Query
- Search trees using *plumule*: a powerful structured query language similar to XPATH
- Combine multiple axes, filters, and subqueries sequentially and recursively to any depth
- Optionally pre-compile queries for increased performance
- Rules Pipelining Engine
- Develop complex lexical parsers with just a few lines of code
- Quickly and easily convert unstructured text into structured, indexed, & searchable tree graphs
- Pre-process text for downstream NLP/AI/ML consumers
- XML Processing
- Features a drop-in replacement for ``ElementTree.XmlParser``
- Full text indices for all elements, attributes, tags, text, etc.
- Search the resulting XML using either XPATH and/or plumule
- Extract *both* ``ElementTree`` and Pawpaw datastructures in one go; with cross-linked nodes between trees
- NLP Support:
- Ideal for *preprocessing* unstructured text for downstream NLP consumption
- Integrates well with other libraries, such as [NLTK](https://www.nltk.org/)
- Efficient pickling and JSON persistence
- A security option allows persistence of index-only data, reinjecting referenced strings during deserialization
- Stable & Defect Free
- Over 5,000 unit tests and counting!
- Pure Python, with only one external dependency: [regex](https://github.com/mrabarnett/mrab-regex)
## Example
With Pawpaw, you can start with flattened text like this:
```
ARTICLE I
Section 1: Congress
All legislative Powers herein granted shall be vested in a Congress of the United States,
which shall consist of a Senate and House of Representatives.
Section 2: The House of Representatives
The House of Representatives shall be composed of Members chosen every second Year by the
People of the several States, and the Electors in each State shall have the Qualifications
requisite for Electors of the most numerous Branch of the State Legislature.
No Person shall be a Representative who shall not have attained to the Age of twenty five
Years, and been seven Years a Citizen of the United States, and who shall not, when elected,
be an Inhabitant of that State in which he shall be chosen.
```
and quickly and easily produce a tree that look like this:
```mermaid
graph TD;
A1["[article]
#quot;ARTICLE I…#quot;"]:::dark_brown --> A1_k["[key]
#quot;I#quot;"]:::dark_brown;
A1--->Sc1["[section]
#quot;Section 1…#quot;"]:::light_brown;
Sc1-->Sc1_k["[key]
#quot;1#quot;"]:::light_brown
Sc1--->Sc1_p1["[paragraph]
#quot;All legislative Powers…#quot;"]:::peach
Sc1_p1-->Sc1_p1_s1["[sentence]
#quot;All legislative Powers…#quot;"]:::dark_green
Sc1_p1_s1-->Sc1_p1_s1_w1["[word]
#quot;All#quot;"]:::light_green
Sc1_p1_s1-->Sc1_p1_s1_w2["[word]
#quot;legislative#quot;"]:::light_green
Sc1_p1_s1-->Sc1_p1_s1_w3["[word]
#quot;Powers#quot;"]:::light_green
Sc1_p1_s1-->Sc1_p1_s1_w4["..."]:::ellipsis
A1--->Sc2["[section]
#quot;Section 2#quot;"]:::light_brown;
Sc2-->Sc2_k["[key]
#quot;2#quot;"]:::light_brown
Sc2--->Sc2_p1["[paragraph]
#quot;The House of…#quot;"]:::peach
Sc2_p1---->Sc2_p1_s1["[sentence]
#quot;The House of…#quot;"]:::dark_green
Sc2_p1_s1-->Sc2_p1_s1_w1["[word]
#quot;The#quot;"]:::light_green
Sc2_p1_s1-->Sc2_p1_s1_w2["[word]
#quot;House#quot;"]:::light_green
Sc2_p1_s1-->Sc2_p1_s1_w3["[word]
#quot;of#quot;"]:::light_green
Sc2_p1_s1-->Sc2_p1_s1_w4["..."]:::ellipsis
Sc2--->Sc2_p2["[paragraph]
#quot;No Person shall…#quot;"]:::peach
Sc2_p2---->Sc2_p2_s1["[sentence]
#quot;No Person shall…#quot;"]:::dark_green
Sc2_p2_s1-->Sc2_p2_s1_w1["[word]
#quot;No#quot;"]:::light_green
Sc2_p2_s1-->Sc2_p2_s1_w2["[word]
#quot;Person#quot;"]:::light_green
Sc2_p2_s1-->Sc2_p2_s1_w3["[word]
#quot;shall#quot;"]:::light_green
Sc2_p2_s1-->Sc2_p2_s1_w4["..."]:::ellipsis
classDef dark_brown fill:#533E30,stroke:#000000,color:#FFFFFF;
classDef light_brown fill:#D2AC70,stroke:#000000,color:#000000;
classDef peach fill:#E4D1AE,stroke:#000000,color:#000000;
classDef dark_green fill:#517D3D,stroke:#000000,color:#FFFFFF;
classDef light_green fill:#90C246,stroke:#000000,color:#FFFFFF;
classDef ellipsis fill:#FFFFFF,stroke:#FFFFFF,color:#000000;
```
You can then search your tree using plumule: a powerful structured query language:
```python
'**[d:section]{**[d:word] & [lcs:power,right]}' # Plumule query to find sections that containing words 'power' or 'right'
```
Try out [this demo](docs/demos/us_constitution) yourself, which shows how easy it is to parse, visualize, and query the US Constitution using Pawpaw.
## Usage
Pawpaw has extensive features and capabilities you can read about in the [Docs](/Docs). As a quick example, say you have some text that would like to perform nlp-like segmentation on.
```python
>>> s = 'nine 9 ten 10 eleven 11 TWELVE 12 thirteen 13'
```
You can use a regular expression for segmentation as follows:
```python
>>> import regex
>>> re = regex.compile(r'(?:(?P(?P(?P\w)+) (?P(?P\d)+))\s*)+')
```
You can then use this regex to feed **Pawpaw**:
```python
>>> import pawpaw
>>> doc = pawpaw.Ito.from_match(re.fullmatch(s))[0]
```
With this single line of code, Pawpaw generates a fully hierarchical, tree of phrases, words, chars, numbers, and digits. You can visualize the tree:
```python
>>> tree_vis = pawpaw.visualization.pepo.Tree()
>>> print(tree_vis.dumps(doc))
(0, 45) '0' : 'nine 9 ten 10 eleven…ELVE 12 thirteen 13'
├──(0, 6) 'phrase' : 'nine 9'
│ ├──(0, 4) 'word' : 'nine'
│ │ ├──(0, 1) 'char' : 'n'
│ │ ├──(1, 2) 'char' : 'i'
│ │ ├──(2, 3) 'char' : 'n'
│ │ └──(3, 4) 'char' : 'e'
│ └──(5, 6) 'number' : '9'
│ └──(5, 6) 'digit' : '9'
├──(7, 13) 'phrase' : 'ten 10'
│ ├──(7, 10) 'word' : 'ten'
│ │ ├──(7, 8) 'char' : 't'
│ │ ├──(8, 9) 'char' : 'e'
│ │ └──(9, 10) 'char' : 'n'
│ └──(11, 13) 'number' : '10'
│ ├──(11, 12) 'digit' : '1'
│ └──(12, 13) 'digit' : '0'
├──(14, 23) 'phrase' : 'eleven 11'
│ ├──(14, 20) 'word' : 'eleven'
│ │ ├──(14, 15) 'char' : 'e'
│ │ ├──(15, 16) 'char' : 'l'
│ │ ├──(16, 17) 'char' : 'e'
│ │ ├──(17, 18) 'char' : 'v'
│ │ ├──(18, 19) 'char' : 'e'
│ │ └──(19, 20) 'char' : 'n'
│ └──(21, 23) 'number' : '11'
│ ├──(21, 22) 'digit' : '1'
│ └──(22, 23) 'digit' : '1'
├──(24, 33) 'phrase' : 'TWELVE 12'
│ ├──(24, 30) 'word' : 'TWELVE'
│ │ ├──(24, 25) 'char' : 'T'
│ │ ├──(25, 26) 'char' : 'W'
│ │ ├──(26, 27) 'char' : 'E'
│ │ ├──(27, 28) 'char' : 'L'
│ │ ├──(28, 29) 'char' : 'V'
│ │ └──(29, 30) 'char' : 'E'
│ └──(31, 33) 'number' : '12'
│ ├──(31, 32) 'digit' : '1'
│ └──(32, 33) 'digit' : '2'
└──(34, 45) 'phrase' : 'thirteen 13'
├──(34, 42) 'word' : 'thirteen'
│ ├──(34, 35) 'char' : 't'
│ ├──(35, 36) 'char' : 'h'
│ ├──(36, 37) 'char' : 'i'
│ ├──(37, 38) 'char' : 'r'
│ ├──(38, 39) 'char' : 't'
│ ├──(39, 40) 'char' : 'e'
│ ├──(40, 41) 'char' : 'e'
│ └──(41, 42) 'char' : 'n'
└──(43, 45) 'number' : '13'
├──(43, 44) 'digit' : '1'
└──(44, 45) 'digit' : '3'
```
And you can search the tree using Pawpaw's *plumule*, a powerful XPATH-like structured query language:
```python
>>> print(*doc.find_all('**[d:digit]'), sep=', ') # all digits
9, 1, 0, 1, 1, 1, 2, 1, 3
>>> print(*doc.find_all('**[d:number]{*[s:i]}'), sep=', ') # all numbers with 'i' in their name
9, 13
```
This example uses regular expressions as a source, however, Pawpaw is able to work with many other input types. For example, you can use libraries such as [NLTK](https://www.nltk.org/) to grow Pawpaw trees, or, you can use Pawpaw's included parser framework to build your own sophisticated parsers quickly and easily.
## Getting Started
### Prerequisites
Pawpaw has been written and tested using Python 3.10. The only dependency is
``regex``, which will be fetched and installed automatically if you install Pawpaw
with pip or conda.
### Installation Options
There are lots of ways to install Pawpaw. Versioned instances that have passed all automated tests are available from [PyPI](https://pypi.org/project/pawpaw/):
1. Install with pip from PyPI:
```
pip install pawpaw
```
2. Install with conda from PyPI:
```
conda activate myenv
conda install git pip
pip install pawpaw
```
Alternatively, you can pull from the main branch at GitHub. This will ensure that you have the latest code, however, the main branch can potentially have internal inconsistencies and/or failed tests:
1. Install with pip from GitHub:
```
pip install git+https://github.com/rlayers/pawpaw.git
```
2. Install with conda from GitHub:
```
conda activate myenv
conda install git pip
pip install git+https://github.com/rlayers/pawpaw.git
```
3. Clone the repo with git from GitHub:
```
git clone https://github.com/rlayers/pawpaw
```
### Verify Installation
Whichever way you fetch Pawpaw, you can easily verify that it is installed correctly. Just open Python prompt and type:
```python
>>> from pawpaw import Ito
>>> Ito('Hello, World!')
Ito(span=(0, 13), desc='', substr='Hello, World!')
```
If your last line looks like this, you are up and running with Pawpaw!
## History & Roadmap
Pawpaw is a rewrite of *desponia*, a now-deprecated Python 2.x segmentation framework that was itself based on a prior framework called *Ito*. Currently in release-candidate status, many components and features are production ready. However, documentation is still being written and some newer features are still undergoing work. A rough outline of which components are finalized is as follows:
- [x] arborform
- [x] itorator
- [x] Desc
- [x] Extract
- [x] Reflect
- [x] Split
- [x] ValueFunc
- [x] postorator
- [x] StackedReduce
- [x] WindowedJoin
- [x] core
- [x] Errors
- [x] Infix
- [x] Ito
- [x] ItoChildren
- [x] nuco
- [x] Span
- [x] Types
- [ ] documentation & examples
- [x] query
- [x] radicle query engine
- [x] plumule
- [ ] nlp
- [x] visualization
- [x] ascibox
- [x] highlighter
- [x] pepo
- [x] sgr
- [x] xml
- [x] XmlHelper
- [x] XmlParser
## Donations
Pawpaw is distributed under the MIT License and is free to use, however, should you wish to help support the continued development of Pawpaw, you may do so via:
| Link | QR Code |
| :-----: | :-----: |
| [](https://www.paypal.com/donate/?hosted_button_id=ALXFKLFU8W2NE) | 
|
## License
Distributed under the MIT License. See [LICENSE](LICENSE) for more information.
## Contacts
Robert L. Ayers: a.nov.guy@gmail.com
## References
* [Matthew Barnett's regex](https://github.com/mrabarnett/mrab-regex)
* [NLTK](https://www.nltk.org/)
[repo]: https://github.com/rlayers/pawpaw
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