ACM DL

Information Systems (TOIS)

Menu

Search Issue
enter search term and/or author name

Archive


ACM Transactions on Information Systems (TOIS), Volume 25 Issue 1, February 2007

Precision recall with user modeling (PRUM): Application to structured information retrieval
B. Piwowarski, P. Gallinari, G. Dupret
Article No.: 1
DOI: 10.1145/1198296.1198297
Standard Information Retrieval (IR) metrics are not well suited for new paradigms like XML or Web IR in which retrievable information units are document elements and/or sets of related documents. Part of the problem stems from the classical...

Named entity translation matching and learning: With application for mining unseen translations
Wai Lam, Shing-Kit Chan, Ruizhang Huang
Article No.: 2
DOI: 10.1145/1198296.1198298
This article introduces a named entity matching model that makes use of both semantic and phonetic evidence. The matching of semantic and phonetic information is captured by a unified framework via a bipartite graph model. By considering various...

An empirical investigation of user term feedback in text-based targeted image search
Joyce Y. Chai, Chen Zhang, Rong Jin
Article No.: 3
DOI: 10.1145/1198296.1198299
Text queries are natural and intuitive for users to describe their information needs. However, text-based image retrieval faces many challenges. Traditional text retrieval techniques on image descriptions have not been very successful. This is mainly...

Creating and exploiting a comparable corpus in cross-language information retrieval
Tuomas Talvensaari, Jorma Laurikkala, Kalervo Järvelin, Martti Juhola, Heikki Keskustalo
Article No.: 4
DOI: 10.1145/1198296.1198300
We present a method for creating a comparable text corpus from two document collections in different languages. The collections can be very different in origin. In this study, we build a comparable corpus from articles by a Swedish news agency and a...

Interest-based personalized search
Zhongming Ma, Gautam Pant, Olivia R. Liu Sheng
Article No.: 5
DOI: 10.1145/1198296.1198301
Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically...