(Enter summary)
Abstract: We describe a fast documentmatcher that matches new documents
to those stored in a database. The matcher lists in order those stored
documents that are most similar to the new document. The new documents
are typically detailed problem descriptions or free form textual
queries of unlimited length, and the stored documents are potential answers
suchasfrequently asked questions or service tips. The method
uses minimal data structures and lightweight scoring algorithms to
compute efficiently ... (Update)
Context of citations to this paper: More
...all remaining vector entries to zero. An alternative approach to selecting a subset of features for a document, described in [Weiss et al. 2000 ] assumes that documents are carefully composed and have effective titles. Title words are always indexed along with the k most...
.... study showed that in an IR environment, where more than one recommendation is made, simpler algorithms can be surprisingly effective[8]. We relied on published results for the alternative, highly specialized collaborative filtering methods. Our method is restricted to...
Cited by: More
Lightweight Collaborative Filtering Method for Binary Encoded.. - Weiss, Indurkhya (2001)
(Correct)
Lightweight Document Clustering - Weiss, White, Apte (2000)
(Correct)
Similar documents (at the sentence level):
14.3%: Lightweight Document Matching for Help-Desk Applications - Weiss, White, Apte, Damerau (2000)
(Correct)
Active bibliography (related documents): More All
0.0: The Maximum-Margin Approach to Learning Text Classifiers -.. - Joachims (2000)
(Correct)
0.0: Theme-Based Retrieval of Web News - Maria (2000)
(Correct)
0.0: Classification of News Stories Using Support Vector Machines - Cooley (1999)
(Correct)
Similar documents based on text: More All
0.3: Text Mining with Decision Trees and Decision Rules - Apte, Damerau, Weiss (1998)
(Correct)
0.3: Towards Language Independent Automated Learning of Text.. - Apte, Damerau, Weiss (1994)
(Correct)
0.3: Automated Learning of Decision Rules for Text Categorization - Apte, Damerau, Weiss (1994)
(Correct)
BibTeX entry: (Update)
S. Weiss, B. White, C. Apt'e, and F. Damerau. Lightweight documentmatching for help-desk applications. IEEE Intelligent Systems, page in press, 2000. http://citeseer.ist.psu.edu/article/weiss99lightweight.html More
@article{ weiss00lightweight,
author = "Sholom M. Weiss and Brian F. White and Chidanand Apte and Fred Damerau",
title = "Lightweight Document Matching for Help-Desk Applications",
journal = "IEEE Intelligent Systems",
volume = "15",
number = "2",
pages = "57-61",
year = "2000",
url = "citeseer.ist.psu.edu/article/weiss99lightweight.html" }
Citations (may not include all citations):
463
Term-weighting approaches in automatic text retrieval (context) - Salton, Buckley - 1997
376
Text Categorization with Support Vector Machines: Learning w..
- Joachims - 1997
269
Toward memorybased reasoning (context) - Stanfill, Waltz - 1986
149
An evaluation of statistical approaches to text categorizati..
- Yang - 1998
118
Glimpse: A tool to search through entire file systems
- Manber, Wu - 1993
76
Boostexter: A boosting-based system for text categorization
- Schapire, Singer - 1999
41
Automated Learning of Decision Rules for Text Categorization (context) - Apt'e, Damerau et al. - 1994
29
Maximizing text-mining performance (context) - Weiss, Apt'e et al. - 1999
17
The smart and sire experimental retrieval systems (context) - Salton, McGill - 1997
2
A self-improving helpdesk service using case-based reasoning.. (context) - Chang, Raman et al. - 1996
1
README File for the Reuters Text Distribution (context) - Lewis - 1995
Documents on the same site (http://www.research.ibm.com/dar/pubs.html): More
R-MINI: An Iterative Approach for Generating Minimal Rules from.. - Hong (1997)
(Correct)
Analysis of Regularized Linear Functions for Classification Problems - Zhang (1999)
(Correct)
Predicting Equity Returns from Securities Data - Apte, Hong (1995)
(Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC