Learning Patent Speak: Investigating Domain-Specific Word Embeddings
Here, we publish word embeddings trained on more than 38 billion tokens of patent documents as part of our paper "Learning Patent Speak: Investigating Domain-Specific Word Embeddings" published at the International Conference on Digital Information Management (ICDIM) 2018. An extended version of our conference paper titled "Domain-Specific Word Embeddings for Patent Classification" has been accepted for publication in the Data Technologies and Applications Journal.
- 100-dimensional embeddings (.vec file) Link
- 100-dimensional embeddings (.bin file) Link.
- Embeddings with 200 dimensions and with 300 dimensions can be provided upon request.
For visualization purposes, we created a subset of 10,000 words and their vectors. This subset can be loaded into tensorflow's projector to explore the embedding space interactively (search for words and display their closest neighbors):
- 10,000 words and their vectors for visualization Link
A patent examiner needs domain-specific knowledge to classify a patent application according to its field of invention. Standardized classification schemes help to compare a patent application to previously granted patents and thereby check its novelty. Due to the large volume of patents, automatic patent classification would be highly beneficial to patent offices and other stakeholders in the patent domain. However, a challenge for the automation of this costly manual task is the patent-specific language use. To facilitate this task, we present domain-specific pre-trained word embeddings for the patent domain. We trained our model on a very large dataset of more than 5 million patents to learn the language use in this domain. We evaluated the quality of the resulting embeddings in the context of patent classification. To this end, we propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings. Experiments on a standardized evaluation dataset show that our approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches.