Code and Datasets for the AAAI 2018 paper "Event Representations with Tensor-based compositions"
- Tensorflow Version 1.4
- Python 3.5
The file get_embeddings.py gives an example script that loads in a pretrained event-tensor model, and given a dataset of SVO triples (in the same format used for training), runs the SVO triples
through the model to produce event embeddings, and then prints the embeddings to a text file.
We use the Open Information Extraction System Ollie to extract triples. The default settings for Ollie will produce triples with long entity and predicate names.
To shorten these, you need to run Ollie with the OpenParse flag called expandExtraction set to false. To do this, you need to create your own main file to run Ollie (replacing this example main in the Ollie source) and run it. The main used for parsing the NYT Gigacorpus is provided for reference in the preproc directory. Parts of this file will need to be replaced as needed if using a different dataset.
The above preprocessing step will output a single tuple per line, which wastes quite a bit of space. In order to convert it to the format used in the training the model (one document per line), use the document_on_line.py script in the preproc directory.
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