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Commit 6755b5f

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author
Arun Tejasvi Chaganty
committed
Initialized with protobuf and tests
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‎.travis.yml

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# this file is *not* meant to cover or endorse the use of travis, but rather to
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# help confirm pull requests to this project.
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language: python
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env:
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- TOXENV=py27
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- TOXENV=py33
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- TOXENV=py34
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install: pip install tox
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script: tox
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notifications:
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email: false

‎MANIFEST.in

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# Include the license file
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include LICENSE.txt
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# Include the data files
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recursive-include data *

‎README.rst

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Stanford CoreNLP Python Bindings
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================================
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This package contains python bindings for [Stanford
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CoreNLP](https://github.com/stanfordnlp/CoreNLP)'s protobuf
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specifications, as generated by `protoc`. These bindings can used to
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parse binary data produced by, e.g., the [Stanford CoreNLP
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server](https://stanfordnlp.github.io/CoreNLP/corenlp-server.html).
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----
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Usage::
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from corenlp_protobuf import Document
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# document.dat contains a serialized Document.
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with open('document.dat', 'r') as f:
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buffer = f.read()
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doc = Document()
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doc.ParseFromString(buffer)
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# You can access the sentences from doc.sentence.
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sentence = doc.sentence[0]
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# You can access any property within a sentence.
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print(sentence.text)
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# Likewise for tokens
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token = sentence.token[0]
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print(token.lemma)

‎corenlp_protobuf/CoreNLP_pb2.py

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‎corenlp_protobuf/__init__.py

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from __future__ import absolute_import
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from google.protobuf.internal.decoder import _DecodeVarint
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from .CoreNLP_pb2 import *
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def parseFromDelimitedString(obj, buf, offset=0):
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"""
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Stanford CoreNLP uses the Java "writeDelimitedTo" function, which
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writes the size (and offset) of the buffer before writing the object.
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This function handles parsing this message starting from offset 0.
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@returns how many bytes of @buf were consumed.
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"""
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size, pos = _DecodeVarint(buf, offset)
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obj.ParseFromString(buf[offset+pos:offset+pos+size])
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return pos+size
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def to_text(sentence):
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"""
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Helper routine that converts a Sentence protobuf to a string from its tokens.
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"""
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text = ""
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for i, tok in enumerate(sentence.token):
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if i != 0:
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text += tok.before
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text += tok.word
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return text

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