{"title":"Error Analysis in an Automated Narrative Information Extraction Pipeline","authors":"Josep Valls-Vargas, Jichen Zhu, Santiago Ontañón","doi":"10.1109/TCIAIG.2016.2575823","DOIUrl":null,"url":null,"abstract":"In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2575823","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Intelligence and AI in Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TCIAIG.2016.2575823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 10
Abstract
In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.
期刊介绍：
Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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