Detecting MultiWord Terms in Patents the same way as Named Entities

Fink, Tobias; Andersson, Linda; Hanbury, Allan

doi:10.34726/pst2019.3

Record link:

https://doi.org/10.34726/pst2019.3
http://hdl.handle.net/20.500.12708/832

Title:

Detecting MultiWord Terms in Patents the same way as Named Entities

Citation:

Fink, T., Andersson, L., & Hanbury, A. (2019). Detecting MultiWord Terms in Patents the same way as Named Entities. In Proceedings of The 1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019). 1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019), Karlsruhe, Germany. https://doi.org/10.34726/pst2019.3

reposiTUm DOI:

10.34726/pst2019.3

CatalogPlus:

AC15614235

Publication Type:

Inproceedings - Full-Paper Contribution

Language:

English

Authors:

Fink, Tobias
Andersson, Linda
Hanbury, Allan

Organisational Unit:

E194 - Institut für Information Systems Engineering

Published in:

Proceedings of The 1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019)

Date (published):

2019

Event name:

1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019)

Event date:

12-Sep-2019

Event place:

Karlsruhe, Germany

Number of Pages:

Keywords:

deep learning; multi word term; patent IR; named entity recognition

Additional information:

In English patent document information retrieval, Multi Word Terms (MWTs) are an important factor in determining how relevant a patent document is for a particular search query. Detecting the correct boundaries for these MWTs is no trivial task and often complicated by the special writing style of the patent domain. In this paper we describe a method for detecting MWTs in patent sentences based on a method for detecting technical named entities using deep learning. On our annotated dataset of 22 patents, our method achieved an average precision of 0.75, an average recall of 0.74 and an average F1 score of 0.74. Further, we argue for the use of domain specific word embedding resources and suggest that our model mostly learns whether individual words should be included in MWTs or not.

License:

CC BY 4.0