Keyphrase Extraction with Sequential Pattern Mining
Abstract
Existing studies show that extracting a complete keyphrase candidate set is the first and crucial step to extract high quality keyphrases from documents. Based on a common sense that words do not repeatedly appear in an effective keyphrase, we propose a novel algorithm named KCSP for document-specific keyphrase candidate search using sequential pattern mining with gap constraints, which only needs to scan a document once and automatically specifies appropriate gap constraints for words without users’ participation. The experimental results confirm that it helps improve the quality of keyphrase extraction.
Cite
Text
Wang et al. "Keyphrase Extraction with Sequential Pattern Mining." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11075Markdown
[Wang et al. "Keyphrase Extraction with Sequential Pattern Mining." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/wang2017aaai-keyphrase/) doi:10.1609/AAAI.V31I1.11075BibTeX
@inproceedings{wang2017aaai-keyphrase,
title = {{Keyphrase Extraction with Sequential Pattern Mining}},
author = {Wang, Qingren and Sheng, Victor S. and Wu, Xindong},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {5003-5004},
doi = {10.1609/AAAI.V31I1.11075},
url = {https://mlanthology.org/aaai/2017/wang2017aaai-keyphrase/}
}