SLING is a parser for annotating text with frame semantic annotations. It is trained on an annotated corpus using Tensorflow and Dragnn.
The parser trainer uses Tensorflow for training. SLING uses the Python 2.7 distribution of Tensorflow, so this needs to be installed. The installed version of protocol buffers needs to match the version used by Tensorflow.
sudo pip install -U protobuf==3.3.0
sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp27-none-linux_x86_64.whlOperating system: Linux
Languages: C++, Python 2.7, assembler
CPU: Intel x64 or compatible
Build system: Bazel
SLING uses Bazel as build system, so you need to install Bazel in order to build the SLING parser.
bazel build -c opt nlp/parserTDB...
The parser needs a frame store and a parser model. The frame store contains the the schemas for the frames and an action table produced by the parser trainer. The parser model contains the trained neural network for the model. The parser model is stored in a Myelin flow file.
The model can be loaded and initialized in the following way:
#include "frame/store.h"
#include "frame/serialization.h"
#include "nlp/document/document-tokenizer.h"
#include "nlp/parser/parser.h"
// Initialize global frame store.
sling::Store commons;
sling::FileDecoder decoder(&commons, "/tmp/parser.sling");
decoder.DecodeAll();
// Load parser model.
sling::nlp::Parser parser;
parser.Load(&commons, "/tmp/parser.flow");
commons.Freeze();
// Create document tokenizer.
sling::nlp::DocumentTokenizer tokenizer;In order to parse some text, it first needs to be tokenized. The document with text, tokens, and frames is stored in a local document frame store.
// Create frame store for document.
sling::Store store(&commons);
sling::nlp::Document document(&store);
// Tokenize text.
string text = "John hit the ball with a bat.";
tokenizer.Tokenize(&document, text);
// Parse document.
parser.Parse(&document);
document.Update();
// Output document annotations.
std::cout << sling::ToText(document.top(), 2);Original authors of the code in this package include:
- Michael Ringgaard
- Rahul Gupta