Detecting Everyday Scenarios in Narrative Texts
This work addresses a central component of language comprehension for natural language processing applications, but it is incremental as it builds on existing techniques for a new task.
The authors tackled the problem of identifying references to everyday scenarios (scripts) in narrative texts, introducing a new task called scenario detection and providing a benchmark dataset with 200 scenarios and a baseline model using topic segmentation and text classification techniques.
Script knowledge consists of detailed information on everyday activities. Such information is often taken for granted in text and needs to be inferred by readers. Therefore, script knowledge is a central component to language comprehension. Previous work on representing scripts is mostly based on extensive manual work or limited to scenarios that can be found with sufficient redundancy in large corpora. We introduce the task of scenario detection, in which we identify references to scripts. In this task, we address a wide range of different scripts (200 scenarios) and we attempt to identify all references to them in a collection of narrative texts. We present a first benchmark data set and a baseline model that tackles scenario detection using techniques from topic segmentation and text classification.