By Topic

Conceptual graphs for the analysis and generation of sentences

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 $33
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)
Paola Velardi ; IBM Italy, Scientific Center, Via Giorgione 159, 00147 Rome, Italy ; Maria Teresa Pazienza ; Mario De' Giovanetti

A system for analyzing and generating Italian texts is under development at the IBM Rome Scientific Center. Detailed semantic knowledge on word-sense patterns is used to relate the linguistic structure of a sentence to a conceptual representation (a conceptual graph). Conceptual graphs are stored in a database and accessed by a natural-language query/answering module. The system analyzes a text supplied by a press-agency-release database. It consists of three modules: a morphological, a syntactic, and a semantic processor. The semantic analyzer uses a conceptual lexicon of word-sense descriptions, currently including about 850 entries. A description is an extended case frame providing the surface semantic patterns (SSP) of a word-sense w. SSPs express both semantic constraints and word-usage information, such as commonly found word patterns, idioms, and metaphoric expressions. SSPs are used by the semantic interpreter to build a conceptual graph of the sentence, which is then accessed by the query-answering and language-generation modules. This paper makes the claim that the SSP approach is viable and necessary to cope with language phenomena in unrestricted domains. Surface patterns are easily acquired inductively from the natural-language corpus rather than deductively from predefined conceptual structures. SSPs map quite complex sentences into surface semantic representations that can be generalized at a subsequent stage. In contrast, the current state of the art does not provide viable theory or methodology to go from superficial to deep structures. This issue is more extensively addressed in the body of the paper.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:32 ,  Issue: 2 )