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https://github.com/dcavar/spacy-json-nlp
spaCy wrapper for JSON-NLP.
https://github.com/dcavar/spacy-json-nlp
json natural-language-processing nlp spacy
Last synced: 2 months ago
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spaCy wrapper for JSON-NLP.
- Host: GitHub
- URL: https://github.com/dcavar/spacy-json-nlp
- Owner: dcavar
- License: apache-2.0
- Created: 2019-03-26T21:31:24.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-11T22:26:08.000Z (over 5 years ago)
- Last Synced: 2024-10-08T01:31:28.475Z (3 months ago)
- Topics: json, natural-language-processing, nlp, spacy
- Language: Python
- Homepage: https://nlp-lab.org/spacyjsonnlp/
- Size: 49.8 KB
- Stars: 12
- Watchers: 5
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# spaCy to JSON-NLP
(C) 2019 by [Damir Cavar], [Oren Baldinger], Maanvitha Gongalla, Anurag Kumar, Murali Kammili
Brought to you by the [NLP-Lab.org]!
This only works with spaCy 2.1.0!
I had the same issue. I downgraded Spacy from 2.1.4 to 2.1.0 and numpy to 1.14.5 to resolve.
## Introduction
Currently this module requires Python 3.6+.
This module provides a [spaCy] v2.1 wrapper for [JSON-NLP]. It takes the [spaCy] output and generates a [JSON-NLP] output. It also provides a Microservice wrapper that allows you to launch the [spaCy] module as a persistent RESTful service using [Flask] or other WSGI-based server.
Since this microservice is built on [spaCy], you will need to have its models download, for example:
python -m spacy download en
python -m spacy download en_core_web_md
## Additional Pipeline Modules
[spaCy] allows for the addition of additional models as pipeline modules. We provide such integrations for coreference and phrase structure trees.
### Anaphora and Coreference Resolution
We provide [HuggingFace] coreference resolution, a fast system tightly integrated into [spaCy]. Note that the first time the parser is run, it will download the coreference models if they are not already present. These models only work for English.
### Phrase Structure Trees (Constituency Parse)
We provide the CPU version of the [benepar] parser, a highly accurate phrase structure parser. Bear in mind it is a Tensorflow module, as such it has a notable start-up time, and relatively high memory requirements (4GB+).
If you have a GPU available, you can install the GPU version of the module with:
pip install --upgrade benepar[gpu]
## Microservice
The [JSON-NLP] repository provides a Microservice class, with a pre-built implementation of [Flask]. To run it, execute:
python spacyjsonnlp/server.py
Since `server.py` extends the [Flask] app, a WSGI file would contain:
from spacyjsonnlp.server import app as application
To disable a pipeline component (such as phrase structure parsing), add
application.constituents = False
The full list of properties that can be disabled or enabled are
- constituents
- dependencies
- coreference
- expressions
The microservice exposes the following URIs:
- /constituents
- /dependencies
- /coreference
- /expressions
- /token_list
These URIs are shortcuts to disable the other components of the parse. In all cases, `tokenList` will be included in the `JSON-NLP` output. An example url is:
http://localhost:5000/dependencies?text=I am a sentence
Text is provided to the microservice with the `text` parameter, via either `GET` or `POST`. If you pass `url` as a parameter, the microservice will scrape that url and process the text of the website.
The [spaCy] language model to use for parsing can be selected with the `spacy_model` parameter.
Here is an example `GET` call:
http://localhost:5000?spacy_model=en&constituents=0&text=I am a sentence.
[Damir Cavar]: http://damir.cavar.me/ "Damir Cavar"
[Oren Baldinger]: https://oren.baldinger.me/ "Oren Baldinger"
[NLP-Lab.org]: http://nlp-lab.org/ "NLP-Lab.org"
[JSON-NLP]: https://github.com/dcavar/JSON-NLP "JSON-NLP"
[Flair]: https://github.com/zalandoresearch/flair "Flair"
[spaCy]: https://spacy.io/ "spaCy"
[NLTK]: http://nltk.org/ "Natural Language Processing Toolkit"
[Polyglot]: https://github.com/aboSamoor/polyglot "Polyglot"
[Xrenner]: https://github.com/amir-zeldes/xrenner "Xrenner"
[CONLL-U]: https://universaldependencies.org/format.html "CONLL-U"
[Flask]: http://flask.pocoo.org/ "Flask"
[HuggingFace]: https://github.com/huggingface/neuralcoref/ "Hugging Face"
[benepar]: https://github.com/nikitakit/self-attentive-parser "Berkeley Neural Parser"