TensorFlow implementation of an arbitrary order Factorization Machine
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TensorFlow implementation of the attr2vec model, based on the following publication:

  • Fabio Petroni, Vassilis Plachouras, Timothy Nugent and Jochen L. Leidner: "attr2vec: : Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines." In: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.

If you use the application please cite the paper.

Modeling input data

The input corpus is represented as two files: Cooccur.csv and Word2Id.csv. The first file follows the original libfm format (http://www.libfm.org) and contains the target vector Y as well as the feature matrix X.

We will use an example to concretely show how to model the input data, using as corpus the following text:

Prime Minister Theresa May will remind her cabinet that discussions must remain private. 
Theresa Mary May is a British politician who has served as Prime Minister.

The folder data_pos contains the modeling of such example corpus using Part-of-Speech (POS) as additional contextual attribute, while the folder data_dependency contains the input data to train dependency-based embeddings.

The Word2Id.csv file contains the symbols vocabulary, and looks like this:


The first column contains the word form or the POS tag, the second column an unique identifier, the third column a meta information to distinguish words from POS tags (i.e., 0 for words, 2 for POS tag).

The Cooccur.csv file looks like this:

1.0 17:1.0 31:1.0 7:1.0 2:1.0
2.0 23:1.0 26:1.0 6:2.0

Please read the libfm manual (http://www.libfm.org/libfm-1.42.manual.pdf) for an extensive description of this format. Here, the first line conveys the information that symbols with id 17, 31, 7, 2 (all with value 1.0) co-occur in the corpus with frequency 1.0.

Train the attr2vec model

To train the attr2vec model on the example data simply run

python train.py

Open the file and edit it to change the paramenters.

The application will write vectors and model metadata in the log folder. You can use TensorBoard to explore the model internals, as follows:

tensorboard --logdir log/