Title :
Usage of Mined Word Associations for Text Retrieval
Author :
Holt, John D. ; Chung, Soon M. ; Li, Yanjun
Author_Institution :
Wright State Univ., Dayton
Abstract :
In this paper, we evaluated the efficacy of mined association rules between words for measuring the similarity between documents to enhance the text retrieval. In our experiments, for each document relevant to a query, we formed a group of documents having at least one common frequent set of words with the answer document. Then we measured the precision of the documents in the same group as an answer set to the corresponding query. This experiment was performed using a corpus of the Text retrieval conference (TREC) and search results. Our experimental results show that the frequent sets of words mined from our test database are useful in ranking query result sets to improve the precision of retrieval.
Keywords :
data mining; query processing; text analysis; mined word association rule; query processing; text retrieval; Artificial intelligence; Association rules; Data mining; Frequency; Indexing; Information retrieval; Itemsets; Marketing and sales; Transaction databases; USA Councils;
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
Print_ISBN :
978-0-7695-3015-4
DOI :
10.1109/ICTAI.2007.171