TY  - JOUR
ID  - SisLab1888
UR  - http://doi.org/10.1109/TKDE.2010.27
IS  - 7
A1  - Phan, Xuan Hieu
A1  - Nguyen, Cam Tu
A1  - Le, Dieu Thu
A1  - Nguyen, Le Minh
A1  - Horiguchi, Susumu
A1  - Ha, Quang Thuy
Y1  - 2011///
N2  - This paper introduces a hidden topic-based framework for processing short and sparse documents (e.g., search result snippets, product descriptions, book/movie summaries, and advertising messages) on the Web. The framework focuses on solving two main challenges posed by these kinds of documents: 1) data sparseness and 2) synonyms/homonyms. The former leads to the lack of shared words and contexts among documents while the latter are big linguistic obstacles in natural language processing (NLP) and information retrieval (IR). The underlying idea of the framework is that common hidden topics discovered from large external data sets (universal data sets), when included, can make short documents less sparse and more topic-oriented. Furthermore, hidden topics from universal data sets help handle unseen data better. The proposed framework can also be applied for different natural languages and data domains. We carefully evaluated the framework by carrying out two experiments for two important online applications (Web search result classification and matching/ranking for contextual advertising) with large-scale universal data sets and we achieved significant results.
JF  - IEEE Transactions on Knowledge and Data Engineering
VL  - 23
SN  - 1041-4347
TI  - A Hidden Topic-Based Framework toward Building Applications with Short Web Documents
SP  - 961
AV  - none
EP  - 976
ER  -