DIKEA: Exploiting Wikipedia for keyphrase extraction

摘要

Automatic keyphrase extraction is the challenging task of assigning keyphrases to documents to capture the main topics. It assists many research areas in the field of text mining - indexing, clustering, and summarisation. A landmark research KEA (Keyphrase Extraction Algorithm) formulated the problem as a supervised machine learning problem and successfully applied a Naive Bayes model to it. KEA showed great promise but its performance is not satisfactory. Its state-of-art extension KEA++ significantly improved its performance but relies on a domain specific vocabulary which is often not available or incomplete for other domains. We present a novel domain-independent system (DIKEA) which makes three main contributions to this field of research: utilising the largest online knowledge source available, Wikipedia, for keyphrase candidate selection; adding new features including a Wikipedia-based feature, link probability; and further boosting performance by using a multilayer perceptron network. Our experiments showed that DIKEA outperformed KEA++ while keeping the overall solution domain-independent. DIKEA was also tested on a benchmark dataset provided by a workshop on Semantic Evaluation (SemEval-2010), allowing comparisons with the 19 other related systems which participated. Our experiments show that DIKEA ranks first when considering only the top 5 keyphrases extracted from each document, and ranks second overall.