Keyphrase Annotation with Graph Co-Ranking
This work addresses the challenge of generating reliable keyphrases for documents, benefiting researchers and practitioners in text analysis by combining the strengths of extraction and assignment methods, though it appears incremental as it builds on existing approaches.
The paper tackles the problem of keyphrase annotation by proposing a new method that integrates both extraction and assignment in a mutually reinforcing manner, achieving statistically significant improvements over state-of-the-art methods in experiments across humanities and social sciences datasets.
Keyphrase annotation is the task of identifying textual units that represent the main content of a document. Keyphrase annotation is either carried out by extracting the most important phrases from a document, keyphrase extraction, or by assigning entries from a controlled domain-specific vocabulary, keyphrase assignment. Assignment methods are generally more reliable. They provide better-formed keyphrases, as well as keyphrases that do not occur in the document. But they are often silent on the contrary of extraction methods that do not depend on manually built resources. This paper proposes a new method to perform both keyphrase extraction and keyphrase assignment in an integrated and mutual reinforcing manner. Experiments have been carried out on datasets covering different domains of humanities and social sciences. They show statistically significant improvements compared to both keyphrase extraction and keyphrase assignment state-of-the art methods.