Awesome
Neural Text-Entity Encoder (NTEE)
Introduction
Neural Text-Entity Encoder (NTEE) is a neural network model that learns embeddings (or distributed representations) of texts and Wikipedia entities. Our model places a text and its relevant entities close to each other in a continuous vector space. The details are explained in the paper Learning Distributed Representations of Texts and Entities from Knowledge Base.
Setup
The following commands install our code and its required libraries:
% pip install Cython
% pip install -r requirements.txt
% python setup.py develop
Download Trained Embeddings
The embeddings used in our experiments can be downloaded from the following links:
- ntee_300_sentence.joblib.gz (300d vectors, 1.8GB, trained on Wikipedia sentences)
- ntee_300_paragraph.joblib.gz (300d vectors, 1.8GB, trained on Wikipedia paragraphs)
These models are Python dict objects serialized with joblib and compressed with gzip.
If you want to use the embeddings in your program, please use ntee.model_reader.ModelReader:
>>> from ntee.model_reader import ModelReader
>>> model = ModelReader('ntee_300_sentence.joblib')
>>> model.get_word_vector(u'apple')
memmap([ -1.81156114e-01, -2.22634017e-01, -8.77011120e-02,
-1.41643256e-01, 2.06349805e-01, -3.81092727e-01,
...
>>> model.get_entity_vector(u'Apple Inc.')
memmap([ -2.48675242e-01, -1.21547781e-01, -1.57411948e-01,
-1.69242024e-01, 3.46656404e-02, -2.03787461e-02,
...
>>> model.get_text_vector(u'Apple, orange, and banana')
array([ -1.90800596e-02, 8.16421525e-05, -5.20865507e-02,
-1.36841238e-02, 2.05799076e-03, 1.26077831e-02,
...
Also, you can directly de-serialize the model file using joblib:
>>> import joblib
>>> model_obj = joblib.load('ntee_300_sentence.joblib')
>>> model_obj.keys()
['word_embedding', 'vocab', 'b', 'W', 'entity_embedding']
Reproducing Sentence Similarity Experiments
SICK:
% wget "http://clic.cimec.unitn.it/composes/materials/SICK.zip"
% unzip SICK.zip
% ntee evaluate_sick ntee_300_sentence.joblib SICK.txt
0.7144 (pearson) 0.6046 (spearman)
STS 2014:
% wget "http://alt.qcri.org/semeval2014/task10/data/uploads/sts-en-gs-2014.zip"
% unzip sts-en-gs-2014.zip
% ntee evaluate_sts ntee_300_sentence.joblib sts-en-test-gs-2014
OnWN: 0.7204 (pearson) 0.7443 (spearman)
deft-forum: 0.5643 (pearson) 0.5490 (spearman)
deft-news: 0.7436 (pearson) 0.6775 (spearman)
headlines: 0.6876 (pearson) 0.6246 (spearman)
images: 0.8204 (pearson) 0.7671 (spearman)
tweet-news: 0.7467 (pearson) 0.6592 (spearman)
NOTE: The ntee command displays a TypeError warning due to the issue descibed here.
Training Embeddings
This section describes how to train a new NTEE model from scratch.
(1) Building Databases
First, we need to download several files and build databases using these files.
% ntee download_dbpedia_abstract_files .
% wget https://s3-ap-northeast-1.amazonaws.com/ntee/pub/enwiki-20160601-pages-articles.xml.bz2
% ntee build_abstract_db . dbpedia_abstract.db
% ntee build_entity_db enwiki-20160601-pages-articles.xml.bz2 entity_db
% ntee build_vocab dbpedia_abstract.db entity_db vocab
(2) Training Pre-trained Embeddings
The pre-trained embeddings can be built using the following two commands:
% ntee word2vec generate_corpus enwiki-20160601-pages-articles.xml.bz2 entity_db word2vec_corpus.txt.bz2
% ntee word2vec train word2vec_corpus.txt.bz2 word2vec_sg_300.joblib
(3) Training NTEE
Now, we can start to train our NTEE embeddings. The training takes approximately six days on NVIDIA K80 GPU.
% ntee train_model dbpedia_abstract.db entity_db vocab --word2vec=word2vec_sg_300.joblib ntee_paragraph.joblib
Reference
If you use the code or the trained embedding in your research, please cite the following paper:
@article{yamada2017ntee,
author = {Yamada, Ikuya and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu},
title = {Learning Distributed Representations of Texts and Entities from Knowledge Base},
journal = {arXiv preprint arXiv:1705.02494},
year = {2017},
}