Code for the paper

PDCDE : Patent Document Clustering with Deep Embeddings
Jaeyoung Kim, Janghyeok Yoon, Eunjeong Park, Sungchul Choi

https://www.researchgate.net/publication/325251122_Patent_Document_Clustering_with_Deep_Embeddings

• KIPRIS dataset

  • KIPRIS dataset consists of abstracts from five categories of US patent
  • Categories : car, cameras, CPUs, memory, graphics.

• The combination used in the paper

  • Task 1 : car-camera(Less relevant class)
  • Task 2 : memory-cpu(Relevant classes)
  • Task 3 : car, camera, cpu, memory, graphics.

• 3 categories task is used KISTA dataset, we will add this dataset soon.

• Tensorflow 1.4.0
• Keras 2.2.0
• nltk 3.3
• pandas 0.23.0
• scikit-learn 0.19.1

#python2
$ pip install -r requirments.txt

#python3
$ pip3 install -r requirments.txt

• category : car_camera, memory_cpu, 5_categories

$ python embedding_patent.py --dataset "category"

$ python train.py --dataset "category"

$ python train.py --dataset "category" --task test

• dataset : categories of dataset. you can select {"car_camera", "memory_cpu", "5_categories"}
• save_embedding_vector : path to the embedding vectors.
• save_weight_path : path to the trained weight.
• dataset_path : path to KPRIS dataset. Default is ./dataset

• window_size : Doc2Vec window size. Default is 5.
• embedding_size : Embedding vector dimension. Default is 50.
• doc_initializer : Doc2Vec word and document initializer. Default is uniform
• negative_sample : Number of negative sampling used0 nce loss. Default is 5.
• doc_lr : Doc2Vec initial learning rate. Default is 0.01.
• doc_batch_size : Doc2Vec batch size. Default is 256.
• doc_epochs : Doc2Vec epochs. Default is 500.

• dec_batch_size : DEC model batch size. Default is 256
• dec_lr : DEC initial learning rate. Default is 0.001
• dec_decay_step : step decay every n epochs.
• layerwise_pretrain_iters : layerwise weight pretrain iterations(greedy layer wise auto encoder). Default is 5000.
• finetune_iters : fine-tunning iteration after layerwise weights pretrain. Default is 5000.