Improving Retrieval of Future-Related Information in Text Collections

People often want to know expected future events related to given real world entities. For supporting users in the process of future scenario analysis, we propose several methods that enable to retrieve and analyze future-related opinions from large text collections. In particular, we focus on time-unreferenced predictions, which do not contain any explicit future time reference and hence are more difficult to be retrieved. As a second contribution, we propose estimating validity of predictions by automatically searching for real world events corresponding to the predictions. This kind of analysis aims to help detect predictions that are no longer valid as well as help estimating prediction accuracy of information sources.

I. Introduction

Lots of future-related information is currently available in text documents. Such information consists of future plans, schedules, predictions, speculations, expectations and so on. The Web or large text collections such as news articles are considered as a reflection of the society. It should be thus possible to harness them for reconstructing collaborative future image held by society. Such kind of social prediction would be generated based on opinions and expectations towards future shared by multiple users according to the “wisdom of the crowds” concept. The results of large scale analysis of future-related information could be useful for individual users in supporting decision making process as well as useful for many domains like futurology, sociological studies, marketing or business intelligence.