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.
|Number of pages||2|
|State||Published - 2017|
|Event||31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States|
Duration: Feb 4 2017 → Feb 10 2017
|Conference||31st AAAI Conference on Artificial Intelligence, AAAI 2017|
|Period||02/4/17 → 02/10/17|