Incorporating Expert Knowledge into Keyphrase Extraction

This paper learns keyphrase taggers for research papers using token-based features incorporating linguistic, surface-form, and document-structure information through sequence labeling and demonstrates that using within document features alone, a tagger trained with ConditionalRandom Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks.

Abstract

Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words.In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surface-form, and document-structure information through sequence labeling. We experimentally illustrate that using within document features alone, our tagger trained with ConditionalRandom Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.