By Topic

A Linguistically-Based Approach to Detect Causality Relations in Unrestricted Text

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

We present an unsupervised linguistically-based approach to discourse relations recognition,  which uses publicly available resources like manually annotated corpora (Discourse Graph  Bank, Penn Discourse TreeBank, RST-DT), as well as empirically derived data from “causally” annotated lexica like LCS, to produce a rule-based algorithm. In our approach we use  the subdivision of Discourse Relations into four subsets – CONTRAST, CAUSE, CONDITION,  ELABORATION, proposed by [1] in their paper where they report results obtained with a  machine-learning approach from a similar experiment against which we compare our results.  Our approach is fully symbolic and is partially derived from the system called GETARUNS,  for text understanding, adapted to a specific task: recognition of Discourse Causal Relations  in free text. We show that in order to achieve better accuracy both in the general task and in  the specific one, semantic information needs to be used besides syntactic structural information. Our approach outperforms results reported in previous papers [2].

Note: PDF Not Yet Available In IEEE Xplore. The document that should appear here is not currently available. The original article file provided to Xplore was corrupt. IEEE Xplore is working to obtain a replacement PDF. That PDF will be posted as soon as it is available. We regret any inconvenience in the meantime.  

Published in:

Artificial Intelligence - Special Session, 2007. MICAI 2007. Sixth Mexican International Conference on

Date of Conference:

4-10 Nov. 2007