A lot of future-related information is available in news articles or Web pages. This information can however differ to large extent and may fluctuate over time. It is therefore difficult for users to manually compare and aggregate it, and to re-construct the most probable course of future events. In this paper we approach a problem of automatically generating summaries of future events related to queries using data obtained from news archive collections or from the Web. We propose two methods, explicit and implicit future-related information detection. The former is based on analyzing the context of future temporal expressions in documents, while the latter relies on detecting periodical patterns in historical document collections. We present a graph-based visualization of future-related information and demonstrate its usefulness through several examples.
Humans have always desired to guess the future in order to adapt their behavior and maximize chances of success. In this paper, we conduct exploratory analysis of future-related information on the web. We focus on the future-related information which is grounded in time, that is, the information on forthcoming events whose expected occurrence dates are already known. We collect data by crawling search engine index and analyze collective view of future time-referenced events discussed on the web.
Abstract-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 the validity of predictions by automatically searching for realworld 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 This kind of analysis aims to help detect predictions that are no longer valid as well as help estimating prediction accuracy of information sources.
Abstract-Future prediction is one of the crucial activities of humans. In this paper, we report the results of exploratory analysis of future-related information on the Web in three different languages: English, Japanese and Polish. We focus on the future-related information which is grounded in time, that is, the information on events whose expected occurrence dates are already known. Our datasets are constructed by crawling search engine indices. We investigate multiple aspects of future-related information in web pages across different languages such as its amount, time span, topics, associated sentiment levels as well as the relation to the future-related content in news articles.
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