Academic Papers

Data analysis with Functional Programming

Experiences using F# for developing analysis scripts and tools over search engine query log data

Stefan Savev and Peter Bailey

Presented at IFL 2010

This paper describes the strengths and weaknesses of functional programming in F# for analysis of large textual datasets.

Search Engine Implementation with Functional Programming

A search engine in a few lines.: yes, we can!

Stefan Savev

SIGIR 2009

This paper shows how easy it is to implement a standard research-style search engine in the F# functional programming language.

Application of Machine learning to Information Retrieval

Document selection methodologies for efficient and effective learning-to-rank

Javed A. Aslam, Evangelos Kanoulas, Virgil Pavlu, Stefan Savev, Emine Yilmaz

SIGIR 2009

This paper evaluates the effect of training data (positive/negative documents) over the quality of the search engine ranking function derived with various machine learning algorithms. This paper finds that current algorithms have problems with imbalanced data and when the diversity between selected documents is small.

Phrasal Queries in Search Engines

Evaluation of phrasal query suggestions

Alan Feuer, Stefan Savev, Javed A. Aslam

CIKM 2007

This paper shows that query suggestions in search engines improves the experience of search engine users. The query suggestions were created from automatically extracted phrases.

Technical Articles

C# Compiler

Hacking the Mono C# Compiler (in Code Project)

This article describes my experience of using the mono C# compiler to obtain the parse tree from a C# source file.