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Rushin Shah


Home Page Resume

About me

My name is Rushin Shah, and I'm a second year LTI Master's student. I work in the field of entity extraction and resolution, and I'm really interested in performing research in these areas on new kinds of data, such as short message streams produced by social media websites. I'm also interested in analyzing the properties of social networks, and these are some of my main motivations for taking the Analysis of Social Media course. Also, I took the Information Extraction course last semester, and I'm interested to see if I can successfully apply the some of the techniques and algorithms taught in that class to social media.

This is my homepage and here's my resume. My areas of interest are machine learning, information extraction, natural language processing, social media and recommendation systems.

Blurb for Information Extraction, 2010:

I want to get an in-depth understanding of the various challenges, ideas and techniques covered in the field of information extraction. I'm currently working with Dr. Robert Frederking on multilingual named entity extraction and co-reference resolution. One particular problem that we're working on right now is cross-document co-reference resolution, and I hope to be able to apply the knowledge that I get from this course towards furthering our research.

Wiki Pages for Analysis of Social Media

April 2011:

March 2011:

Presentation for Analysis of Social Media

Wiki Pages for Information Extraction

September 2010:

Frietag 2000 Maximum Entropy Markov Models for Information Extraction and Segmentation

Lafferty 2001 Conditional Random Fields

Within Document Coreference (WDC)

October 2010:

Cross Document Coreference (CDC)

ACE 2005 Dataset

Relation Extraction

November 2010:

Ravichandran and Hovy, ACL 2002: Learning Surface Text Patterns for a Question Answering System

Huang et al, ACL 2009: Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering

Huang et al, Coling 2010: Enhancing Cross Document Coreference of Web Documents with Context Similarity and Very Large Scale Text Categorization