Analysis of Relevant Text Fragments for Different Search Task Types

Abstract

This paper investigates the trend of relevant text fragments by task type. The search results of fine-grained information retrieval systems propose not documents but text fragments. We hypothesize that the properties of relevant text fragments depend on the task type. To reveal these properties, we evaluate a relevant text fragment to judge (1) its granularity (e.g., word, phrase, or sentence) and (2) its structural complexity. Our analysis shows that a task type based on more complex information needs has a larger granularity of relevant text fragments. On the other hand, the complexity of task type’s information needs does not necessarily correlate with the structural complexity of the relevant text fragments.