khaack / emnlp2017-relation-extraction

Context-Aware Representations for Knowledge Base Relation Extraction

Context-Aware Representations for Knowledge Base Relation Extraction

Relation extraction on an open-domain knowledge base

Accompanying repository for our EMNLP 2017 paper (full paper). It contains the code to replicate the experiments and the pre-trained models for sentence-level relation extraction.

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

Please use the following citation:

@inproceedings{TUD-CS-2017-0119,
	title = {Context-Aware Representations for Knowledge Base Relation Extraction},
	author = {Sorokin, Daniil and Gurevych, Iryna},
	booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
	pages = {(to appear)},
	year = {2017},
	location = {Copenhagen, Denmark},
}

Paper abstract:

We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24% on a held-out set of relations.

Please, refer to the paper for more details.

The dataset described in the paper can be found here:

• https://www.ukp.tu-darmstadt.de/data/lexical-resources/wikipedia-wikidata-relations/

Contacts:

If you have any questions regarding the code, please, don't hesitate to contact the authors or report an issue.

• Daniil Sorokin, [email protected]
• https://www.ukp.tu-darmstadt.de
• https://www.tu-darmstadt.de

Demo:

You can try out the model on single sentences in our demo:

http://semanticparsing.ukp.informatik.tu-darmstadt.de:5000/

Project structure:

relation_extraction/
├── apply-model.py
├── eval.py
├── model-train-and-test.py
├── notebooks
├── optimization_space.py
├── parsing
│   ├── semanticparsing.py
│   └── sp_models.py
├── semanticgraph
│   ├── graph_utils.py
│   ├── io.py
│   └── vis_utils.py
├── stanford_tag_dataset.py
└── utils
    ├── embedding_utils.py
    ├── evaluation_utils.py
    └── graph.py
resources/
└── property_blacklist.txt

File	Description
relation_extraction/	Main Python module
relation_extraction/parsing	Models for joint relation extraction
relation_extraction/semanticgraph	IO and processing for relation graphs
relation_extraction/utils	IO and evaluation utils
resources/	Necessary resources

Setup:

1. We recommend that you setup a new pip environment first: http://docs.python-guide.org/en/latest/dev/virtualenvs/

2. Check out the repository and run:

pip3 install -r requirements.txt

3. Set the Keras (deep learning library) backend to Theano (even deeper deep learning library) with the following command:

export KERAS_BACKEND=theano

You can also permanently change Keras backend (read more: https://keras.io/backend/).

4. Download the data, if you want to replicate the experiments from the paper

Pre-trained models:

• You can download the models that were used in the experiments here
• We will soon make available updated models and will publish usage instructions

Requirements:

• Python 3.4
• See requirements.txt for library requirements.

License:

• Apache License Version 2.0