Story retrieval and comparison using concept patterns

Traditional story comparison uses key words to determine similarity. However, the use of key words misses much of what makes two stories alike. The method we have developed use high level concept patterns, which are comprised of multiple events, and compares them across stories. Comparison based on...

Full description

Bibliographic Details
Main Authors: Krakauer, Caryn E., Winston, Patrick Henry
Format: Article
Language:en_US
Published: © The Association for Computational Linguistics 2022
Subjects:
Online Access:http://narrative.csail.mit.edu/cmn12/proceedings.pdf
https://hdl.handle.net/1721.1/141723
_version_ 1826189321083813888
author Krakauer, Caryn E.
Winston, Patrick Henry
author_facet Krakauer, Caryn E.
Winston, Patrick Henry
author_sort Krakauer, Caryn E.
collection MIT
description Traditional story comparison uses key words to determine similarity. However, the use of key words misses much of what makes two stories alike. The method we have developed use high level concept patterns, which are comprised of multiple events, and compares them across stories. Comparison based on concept patterns can note that two stories are similar because both contain, for example, revenge and betrayal concept patterns, even though the words revenge and betrayal do not appear in either story, and one may be about kings and kingdoms while the other is about presidents and countries. Using a small corpus of 15 conflict stories, we have shown that similarity measurement using concept patterns does, in fact, differ substantially from similarity measurement using key words. The Goldilocks principle states that features should be of intermediate size; they should be not too big, and they should not too small. Our work can be viewed as adhering to the Goldilocks principle because concept patterns are features of intermediate size, hence not so large as an entire story, because no story will be exactly like another story, and not so small as individual words, because individual words tend to be common in all stories taken from the same domain. While our goal is to develop a human competence model, we note application potential in retrieval, prediction, explanation, and grouping.
first_indexed 2024-09-23T08:13:16Z
format Article
id mit-1721.1/141723
institution Massachusetts Institute of Technology
language en_US
last_indexed 2024-09-23T08:13:16Z
publishDate 2022
publisher © The Association for Computational Linguistics
record_format dspace
spelling mit-1721.1/1417232022-04-07T03:43:27Z Story retrieval and comparison using concept patterns Krakauer, Caryn E. Winston, Patrick Henry Goldilocks principle story retrieval intermediate features concept patterns Traditional story comparison uses key words to determine similarity. However, the use of key words misses much of what makes two stories alike. The method we have developed use high level concept patterns, which are comprised of multiple events, and compares them across stories. Comparison based on concept patterns can note that two stories are similar because both contain, for example, revenge and betrayal concept patterns, even though the words revenge and betrayal do not appear in either story, and one may be about kings and kingdoms while the other is about presidents and countries. Using a small corpus of 15 conflict stories, we have shown that similarity measurement using concept patterns does, in fact, differ substantially from similarity measurement using key words. The Goldilocks principle states that features should be of intermediate size; they should be not too big, and they should not too small. Our work can be viewed as adhering to the Goldilocks principle because concept patterns are features of intermediate size, hence not so large as an entire story, because no story will be exactly like another story, and not so small as individual words, because individual words tend to be common in all stories taken from the same domain. While our goal is to develop a human competence model, we note application potential in retrieval, prediction, explanation, and grouping. This material is based on work supported by the U.S. Office of Naval Research, Grant No. N00014-09-1-0597. Any opinions, findings, conclusions or recommendations therein are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research. 2022-04-06T17:38:11Z 2022-04-06T17:38:11Z 2012-05-26 Article http://narrative.csail.mit.edu/cmn12/proceedings.pdf https://hdl.handle.net/1721.1/141723 Krakauer, C. E., & Winston, P. H. (2012). Story retrieval and comparison using concept patterns. Proceedings of the 3rd International Workshop on Computational Models of Narrative (CMN'12), Turkey, 119–124. en_US Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf © The Association for Computational Linguistics
spellingShingle Goldilocks principle
story retrieval
intermediate features
concept patterns
Krakauer, Caryn E.
Winston, Patrick Henry
Story retrieval and comparison using concept patterns
title Story retrieval and comparison using concept patterns
title_full Story retrieval and comparison using concept patterns
title_fullStr Story retrieval and comparison using concept patterns
title_full_unstemmed Story retrieval and comparison using concept patterns
title_short Story retrieval and comparison using concept patterns
title_sort story retrieval and comparison using concept patterns
topic Goldilocks principle
story retrieval
intermediate features
concept patterns
url http://narrative.csail.mit.edu/cmn12/proceedings.pdf
https://hdl.handle.net/1721.1/141723
work_keys_str_mv AT krakauercaryne storyretrievalandcomparisonusingconceptpatterns
AT winstonpatrickhenry storyretrievalandcomparisonusingconceptpatterns