Open Collections

Evaluating open relation extraction over conversational texts

University of British Columbia

In this thesis, for the first time the performance of Open IE systems on conversational data has been studied. Due to lack of test datasets in this domain, a method for creating the test dataset covering a wide range of conversational data has been proposed. Conversational text is more complex and challenging for relation extraction because of its cryptic content and ungrammatical colloquial language. As a consequence text simplification has been used as a remedy to empower Open IE tools for relation extraction. Experimental results show that text simplification helps OLLIE, a state of the art for relation extraction, find new relations, extract more accurate relations and assign higher confidence scores to correct relations and lower confidence scores to incorrect relations for most datasets. Results also show some conversational modalities such as emails and blogs are easier for relation extraction task while people reviews on products is the most difficult modality.

Text

2014-02-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/45978

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/