HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID

Patrice Lopez (1, 2) , Laurent Romary (1, 2)

1 IDSL - Institut für Deutsche Sprache und Linguistik
2 Inria Saclay - Ile de France

The Semeval task 5 was an opportunity for experimenting with the key term ex- traction module of GROBID, a system for extracting and generating bibliographical information from technical and scientific documents. The tool first uses GROBID's facilities for analyzing the structure of sci- entific articles, resulting in a first set of structural features. A second set of fea- tures captures content properties based on phraseness, informativeness and keyword- ness measures. Two knowledge bases, GRISP and Wikipedia, are then exploited for producing a last set of lexical/semantic features. Bagged decision trees appeared to be the most efficient machine learning algorithm for generating a list of ranked key term candidates. Finally a post rank- ing was realized based on statistics of co- usage of keywords in HAL, a large Open Access publication repository.

Domaines: Informatique et langage [cs.CL]

Patrice Lopez, Laurent Romary. HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID. SemEval 2010 Workshop, ACL SigLex event, Jul 2010, Uppsala, Sweden. 4 p. ⟨inria-00493437⟩