From f92aba94be3a281b4bfed2c064b77d2e86076a31 Mon Sep 17 00:00:00 2001 From: anbestCL <43368821+anbestCL@users.noreply.github.com> Date: Wed, 10 Jul 2019 23:13:11 +0200 Subject: [PATCH] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 6f18d5e..ac5318e 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,14 @@ # FOFE Character Encoding -## Structure of project +## Project This project's first aim is to implement a neural layer in Pytorch which performs the FOFE method on character level described in [Zhang et al. (2015)](http://www.aclweb.org/anthology/P15-2081) to embed to the words. This layer is then passed to a bidirectional GRU architecture. In a second step the new FOFE layer is compared to a classical, randomly initialised embedding layer. The two architectures are tested on the English [ATIS dataset](https://github.com/Microsoft/CNTK/tree/master/Examples/LanguageUnderstanding/ATIS/Data) and on parts of the German [Tiger Corpus](http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/download/start.html). -## Structure of repository +## Repository -The [source](src) folder includes python and bash scripts designed for the different configurations. There is a main [tagger](src/py-scripts/tagger.py) script which uses the [FOFE](../src/py-scripts/fofe_model.py)_ or the [Classic](src/py-scripts/classic_model.py) depending on the model to be trained. [Data preparation](src/py-scripts/prep.py) for both corpora is done in advance. +The [source](src) folder includes python and bash scripts designed for the different configurations. There is a main [tagger](src/py-scripts/tagger.py) program which uses the [FOFE](src/py-scripts/fofe_model.py)_ or the [Classic](src/py-scripts/classic_model.py) depending on the model to be trained. [Data preparation](src/py-scripts/prep.py) for both corpora is done in advance. To test different parameter configurations there is a wrapper class for the tagger module which can be used for [hyper paramter optimisation](src/py-scripts/hyperopt.py). ### Implementation