Authors:
Mika Timonen
1
;
Timo Toivanen
2
;
Yue Teng
3
;
Chao Chen
3
and
Liang He
3
Affiliations:
1
VTT Technical Research Centre of Finland and University of Helsinki, Finland
;
2
VTT Technical Research Centre of Finland, Finland
;
3
East China Normal University, China
Keyword(s):
Keyword Extraction, Machine Learning, Short Documents, Term Weighting, Text Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Context Discovery
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
With the rise of user created content on the Internet, the focus of text mining has shifted. Twitter messages and product descriptions are examples of new corpora available for text mining. Keyword extraction, user modeling and text categorization are all areas that are focusing on utilizing this new data. However, as the documents within these corpora are considerably shorter than in the traditional cases, such as news articles, there are also new challenges. In this paper, we focus on keyword extraction from documents such as event and product descriptions, and movie plot lines that often hold 30 to 60 words. We propose a novel unsupervised keyword extraction approach called Informativeness-based Keyword Extraction (IKE) that uses clustering and three levels of word evaluation to address the challenges of short documents. We evaluate the performance of our approach by using manually tagged test sets and compare the results against other keyword extraction methods, such as CollabRan
k, KeyGraph, Chi-squared, and TF-IDF. We also evaluate the precision and effectiveness of the extracted keywords for user modeling and recommendation and report the results of all approaches. In all of the experiments IKE out-performs the competition.
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