Neural_Tensor_Network_For_KB_Completion v0.1

A Neural Tensor Network for Knowledge Base Completion as described in the paper "Reasoning With Neural Tensor Networks For Knowledge Base Completion" (http://nlp.stanford.edu/~socherr/SocherChenManningNg_NIPS2013.pdf by Richard Socher*, Danqi Chen*, Christopher D. Manning and Andrew Y. Ng. (http://www.socher.org/index.php/Main/ReasoningWithNeuralTensorNetworksForKnowledgeBaseCompletion)

• Original Matlab code: download via webarchive: https://web.archive.org/web/20140807011802/http://www-nlp.stanford.edu/~socherr/codeDeepDB.zip

• Python version: https://github.com/siddharth-agrawal/Neural-Tensor-Network

• Java Implementation: here,

  • based on ND4j (http://nd4j.org/) as computation libary for linear algebra (as alternative to numpy and scipy in Python)
  • based on LBFGS Optimization based on https://github.com/aria42/nlp-utils
  • jmatio for loading *.mat word embeddings

• Before running the code:

• Run Configurations / Arguments / add "-Xmx2G" in the box at the bottom

• Edit path to training data

• Download the jar files for the build path: https://files.gitter.im/c0der1337/CIxq/build_path_libs.zip

Additional information / resources:

• Architecture of NTN:
• digital: [WE dimension=6, slice size=3] http://fs1.directupload.net/images/150519/5owgxnhp.png
• draft: http://fs2.directupload.net/images/150428/hjxlxzom.jpg
• Classdiagram: http://fs2.directupload.net/images/150519/hqjyguo6.png

Main classes:

• Run_NTN: start the project
• NTN: Neural Tensor Network with Cost/Loss function
• Data_Factory: responsible for loading, providing and managing data

Accuracy with data from Socher et. al 2013: Freebase: Wordbase

Roadmap / next steps:

• Code review
• Maven integration
• Integration in DL4j Framework
• Multilingual support
• Multilingual training data: spanish and german tripples and word vectors
• Improvements and Experiments