Term: Spring 2017

• Team #12
• Projec title: Entity Resolution
• Team members
  • Kai Chen (Presenter)
  • Senyao Han
  • Kexin Nie
  • Yini Zhang
  • Chenyun Zhu
• Project summary: In our project, we studied entity resolution through two academic papers which introduce two methods. Paper 2 is about using linear svm, with its own unique way of evaluation: comparing the performance of different variables through its average accuracy. For paper 5, we studied author disambiguation using error-driven machine learning with a ranking loss function. The features we used for are cosine similarity between the words and Euclidean distance in Word2vec model. The detailed methods we applied are as follow: Clusterwise Scoring Function as the partition criterion, Error-driven Online Training to generate training examples and Ranking Perceptron as the loss function.

• Paper 5 Algorithm:

Contribution statement: (default) All team members contributed equally in all stages of this project. All team members approve our work presented in this GitHub repository including this contributions statement.

Paper2 SVM: Senyao Han, Kexin Nie

Paper5: Kai Chen, Yini Zhang, Chenyun Zhu

Following suggestions by RICH FITZJOHN (@richfitz). This folder is orgarnized as follows.

proj/
├── lib/
├── data/
├── doc/
├── figs/
└── output/

Please see each subfolder for a README file.