The document aboutness problem asks for creating a succinct representation of a document’s subject matter via keywords, sentences or entities drawn from a Knowledge Base. In this paper we propose an approach to solve this problem which improves the known solutions over all known datasets [4,19]. It is based on a wide and detailed experimental study of syntactic and semantic features drawn from the input document thanks to the use of some IR/NLP tools. To encourage and support reproducible experimental results on this task, we will make accessible our system via a public API: this is the first, and best performing, tool publicly available for the document aboutness problem.
CITATION STYLE
Ponza, M., Ferragina, P., & Piccinno, F. (2017). Document aboutness via sophisticated syntactic and semantic features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10260 LNCS, pp. 441–453). Springer Verlag. https://doi.org/10.1007/978-3-319-59569-6_53
Mendeley helps you to discover research relevant for your work.