Multi-lingual Analysis of Future-Related Information on the Web

Jatowt, Adam; Kawai, Hideki; Kanazawa, Kensuke; Tanaka, Katsumi; Kunieda, Kazuo; Yamada, Keiji

doi:10.1109/culturecomputing.2013.13

Cited by 6 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[Kawai et al 2010] analyzed effective ways to automatically categorize future-related information in documents using supervised learning, and [Kanhabua et al 2011] proposed a learned ranking model for news predictions that considers the weighted sum of a number of feature scores. Some research [Jatowt et al 2010, Campos et al 2011a, Jatowt et al 2013b has also been conducted to understand the characteristics of the future-related information in news articles and on the web. For instance, [Jatowt et al 2010] analyzed future-related information on the web by showing the distribution of hit counts obtained from web search engines for queries containing future dates as well as by listing terms that appear frequently with different future years.…”

Section: Future-related Information Retrievalmentioning

confidence: 99%

“…This work compares the amount and the typical topics of information related to the near or distant future and finds that significant amount of near-term future-related information refers to the events scheduled to happen until the end of current calendar year. Their study was later extended by the cross-lingual comparison and the sentiment analysis of future-related information on the web as well as topical comparison with the futurerelated content in news articles [Jatowt et al, 2013b]. [Jatowt and Yeung 2011] studied the time range to which future references refer on average in news articles and the granularity of these temporal expressions as a function of the temporal distance from the article creation date (Fig.…”

Section: Future-related Information Retrievalmentioning

confidence: 99%

See 1 more Smart Citation

Survey of Temporal Information Retrieval and Related Applications

et al. 2014

Self Cite

View full text Add to dashboard Cite

Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remain undeveloped.

show abstract

Section: Future-related Information Retrievalmentioning

confidence: 99%

Section: Future-related Information Retrievalmentioning

confidence: 99%

Survey of Temporal Information Retrieval and Related Applications

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the above examples, the patterns that were matched comprise those studied in previous research [3], [5], [6]. These include time-related expressions ("late May," "from December 1," "from March 1, 1996") and future reference expressions ("is expected," "is planned to," "is likely to").…”

Section: Inquiry Into Extracted Future Reference Patternsmentioning

confidence: 88%

“…Kanazawa et al [5] focused on extracting unreferenced future time expressions from a large collection of text, and proposed a method for estimating the validity of the prediction by automatically searching for a real-world event corresponding to the predicted one. Jatowt et al [6] studied the relation between future news written in English, Polish, and Japanese using keywords queried on the web. Popescu et al [7] investigated significant changes in the distribution of terms within the Google Books corpus and their relationship with emotion words across a wide time span.…”

Section: Previous Researchmentioning

confidence: 99%

A Method for Extraction of Future Reference Sentences Based on Semantic Role Labeling

Nakajima

Ptaszyński

Honma

et al. 2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYIn everyday life, people use past events and their own knowledge in predicting probable unfolding of events. To obtain the necessary knowledge for such predictions, newspapers and the Internet provide a general source of information. Newspapers contain various expressions describing past events, but also current and future events, and opinions. In our research we focused on automatically obtaining sentences that make reference to the future. Such sentences can contain expressions that not only explicitly refer to future events, but could also refer to past or current events. For example, if people read a news article that states "In the near future, there will be an upward trend in the price of gasoline," they may be likely to buy gasoline now. However, if the article says "The cost of gasoline has just risen 10 yen per liter," people will not rush to buy gasoline, because they accept this as reality and may expect the cost to decrease in the future. In the following study we firstly investigate future reference sentences in newspapers and Web news. Next, we propose a method for automatic extraction of such sentences by using semantic role labels, without typical approaches (temporal expressions, etc.). In a series of experiments, we extract semantic role patterns from future reference sentences and examine the validity of the extracted patterns in classification of future reference sentences. key words: natural language processing, future reference expressions, future prediction, information extraction

show abstract

“…In general, mining history-related knowledge is another popular direction of study. For example, several works try to find beneficial information from large amounts of data by evaluat-ing the significance of historical entities [31], timestamping entities [32], analyzing trends [29], or trying to predict future from past events [33,49,50].…”

Section: Temporal Analysismentioning

confidence: 99%

Analyzing history-related posts in twitter

Sumikawa

Jatowt

2020

Int J Digit Libr

Self Cite

View full text Add to dashboard Cite

Microblogging platforms such as Twitter have been increasingly used nowadays to share information between users. They are also convenient means for propagating content related to history. Hence, from the research viewpoint they can offer opportunities to analyze the way in which users refer to the past, and how as well when such references appear and what purposes they serve. Such study could allow to quantify the interest degree and the mechanisms behind content dissemination. We report the results of a large scale exploratory analysis of history-oriented posts in microblogs based on a 28-month-long snapshot of Twitter data. The results can increase our understanding of the characteristics of history-focused content sharing in Twitter. They can also be used for guiding the design of content recommendation systems as well as time-aware search applications.

show abstract

Multi-lingual Analysis of Future-Related Information on the Web

Cited by 6 publications

References 13 publications

Survey of Temporal Information Retrieval and Related Applications

Survey of Temporal Information Retrieval and Related Applications

A Method for Extraction of Future Reference Sentences Based on Semantic Role Labeling

Analyzing history-related posts in twitter

Contact Info

Product

Resources

About