Archive for February, 2007
POTW 2/26/07: Overview of the TREC 2003 Question Answering Track by Voorhees
This week’s paper can be found at http://www.inf.ed.ac.uk/teaching/courses/tts/papers/QA.OVERVIEW.pdf.
This paper should provide us an introduction to the QA problem and provide some background on evaluation. From here, we will start looking into the different approaches taken for question answering.
natural language processing, QA, question answering
Popularity: 3% [?]Technorati Tags: natural language processing, QA, question answeringPopularity: 3% [?]
Discussion of LexRank by Erkan and Radev
POTW 2/18/07: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
The LexRank paper by Erkan and Radev is another PageRank/Graph Theory based approach to working with text, this time applied to the task of summarization.
Key parts of sections 1 and 2 discuss the general problem of corpus-based summarization. Unlike TextRank by Mihalcea, Erkan and Radev [...]Popularity: 7% [?]
POTW 2/18/07: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
The POTW for 2/18/07 is another graph-based approach, this time by another leader in this area, Dragomir Radev. The paper is
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
Enjoy!
Popularity: 5% [?]Popularity: 5% [?]
POTW 2/11/07: Discussion of sections 5-8 of Minkov, et. al
POTW 2/11/07: Contextual Search and Name Disambiguation in Email Using Graphs
Discussion of Sections 5 through 8
The remaining sections of this paper are discussions of two applications of the algorithms plus the body of related work and conclusions that can be drawn from the work. Section 5 gives the details on what corpora were used [...]Popularity: 6% [?]
POTW 2/11/07: Discussion of sections 1-4 of Minkov, et. al
Intro
This week we are reading Contextual Search and Name Disambiguation in Email Using Graphs
by Einat Minkov, William W. Cohen and Andrew Y Ng, all of Carnegie Mellon University. Like the past few papers, this paper also focuses on how to use graph theory to solve some common NLP papers. Unlike the past few [...]Popularity: 6% [?]
