TY - JOUR
T1 - Multi-Task learning of keyphrase boundary classification
AU - Augenstein, Isabelle
AU - Sogaard, A.
PY - 2017
Y1 - 2017
N2 - Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far un-derexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases
AB - Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far un-derexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases
U2 - 10.18653/v1/P17-2054
DO - 10.18653/v1/P17-2054
M3 - Article
JO - Association for Computational Linguistics
JF - Association for Computational Linguistics
ER -