Extracting and Querying Relations in Scientific Papers on Language Technology

We describe methods for extracting interesting factual relations from scientific texts in computational linguistics and language technology taken from the ACL Anthology. We use a hybrid NLP architecture with shallow preprocessing for increased robustness and domainspecific, ontology-based named entity recognition, followed by a deep HPSG parser running the English Resource Grammar (ERG). The extracted relations in the MRS (minimal recursion semantics) format are simplified and generalized using WordNet. The resulting s' are stored in a database from where they can be retrieved (again using abstraction methods) by relation-based search. The query interface is embedded in a web browser-based application we call the Scientist's Workbench. It supports researchers in editing and online-searching scientific papers.

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Ulrich Schäfer, Hans Uszkoreit, Christian Federmann, Torsten Marek, Yajing Zhang
• Keywords: Deep Hpsg Parser, Education, Interesting Factual Relations, LREC 2008, Named Entity Recognition