Code for paper "Entity Linking via Joint Encoding of Types, Descriptions, and Context", EMNLP '17
For accurate entity linking, we need to capture the various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Further, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-art-systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively "embed" entities that are new to the KB, and is able to link its mentions accurately.
- Python 3.4
- Tensorflow 0.11 / 0.12
- numpy
- CogComp-NLPy
- Resources - Pretrained models, vectors, etc.
- Clone the code repository
- Download the resources folder.
- In
config/config.iniset the correct path to the resources folder you just downloaded - Run using:
python3 neuralel.py --config=configs/config.ini --model_path=PATH_TO_MODEL_IN_RESOURCES --mode=inference
The file sampletest.txt in the resources folder contains the text to be entity-linked. Currently we only support linking for a single document. Make sure the text in sampletest.txt is a single doc in a single line.
CogComp-NLPy is needed to detect named-entity mentions using NER. To install:
pip install cython
pip install ccg_nlpy
To install tensorflow 0.12:
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp34-cp34m-linux_x86_64.whl
(Regular) pip install --upgrade $TF_BINARY_URL
(Conda) pip install --ignore-installed --upgrade $TF_BINARY_URL