This project is a fork of biplab-iitb
Warning
CLI is only for example purpose don't use for long running jobs.
Get the very old code in devbranch or prior stable version oldVersion.
Practical Natural Language Processing Tools for Humans. practNLPTools is a pythonic library over SENNA and Stanford Dependency Extractor.
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| travis |
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| Documentation |
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| dependency |
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| blocker Pyupbot | |blocker status |
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Note
After version 0.3.0+ pntl should able to store the result into database for later usage if needed by installing below dependency.
pip install git+https://github.com/jawahar273/snowbase.git
To downlard the stanford-parser from github automatically and placing them inside the install direction.
pntl -I true
# downlards required file from github.To run predefine example in batch mode(which has more than one list of examples).
pntl -SE home/user/senna -B trueBatch mode means listed sentences.
..code:
# Example structure for predefine
# Sentences in the code.
sentences = [
"This is line 1",
"This is line 2",
]To run predefine example in non batch mode.
pntl -SE home/user/sennaTo run user given example using -S is
pntl -SE home/user/senna -S 'I am gonna make him an offer he can not refuse.'- Semantic Role Labeling.
- Syntactic Parsing.
- Part of Speech Tagging (POS Tagging).
- Named Entity Recognisation (NER).
- Dependency Parsing.
- Shallow Chunking.
- Skip-gram(in-case).
- find the senna path if is install in the system.
- stanford parser and depPaser file into installed direction.
- tag2file(new)
- creating depParser for corresponding os environment
- custome input format for stanford parser insted of tree format
- Fast: SENNA is written is C. So it is Fast.
- We use only dependency Extractor Component of Stanford Parser, which takes in Syntactic Parse from SENNA and applies dependency Extraction. So there is no need to load parsing models for Stanford Parser, which takes time.
- Easy to use.
- Platform Supported - Windows, Linux and Mac
- Automatic finds stanford parsing jar if it is present in install path[pntl].
Note
SENNA pipeline has a fixed maximum size of the sentences that it can read. By default it is 1024 token/sentence. If you have larger sentences, changing the MAX_SENTENCE_SIZE value in SENNA_main.c should beconsidered and your system specific binary should be rebuilt. Otherwise this could introduce misalignment errors.
Requires:
A computer with 500mb memory, Java Runtime Environment (1.7 preferably, works with 1.6 too, but didnt test.) installed and python.
Linux:
run:
sudo python setup.py installwindows:
run this commands as administrator:
python setup.py install
By using the time command in ubuntu on running the testsrl.py on this link and along with tools.py on pntl
| pntl | NLTK-senna | |
|---|---|---|
| at fist run | ||
| real 0m1.674s | real 0m2.484s | |
| user 0m1.564s | user 0m1.868s | |
| sys 0m0.228s | sys 0m0.524s | |
| at second run | ||
| real 0m1.245s | real 0m3.359s | |
| user 0m1.560s | user 0m2.016s | |
| sys 0m0.152s | sys 0m1.168s |
Note
This benchmark may diffrent from system to sytem. The result produced here is from ububtu 4Gb RAM and i3 process.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.