Using Contextual Information to Improve Search in Email Archives

Weerkamp, Wouter; Balog, Krisztian; Rijke, Maarten de

doi:10.1007/978-3-642-00958-7_36

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…A number of email mining studies have focused on people-related tasks, including name recognition and reference resolution, contact information extraction, identity modeling and resolution, role discovery, and expert identification, as well as the generation of access to large-scale email archives from multiple viewpoints by using a faceted search [115]. Moreover, some significant applications of email mining include tasks, such as filtering emails based on priority and identifying spam and phishing emails, as well as automatic answering, thread summarization, contact analysis, email visualization, network property analysis, and categorization [116,117].…”

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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“…This is especially important since a large portion of our digital history is textual information. Weerkamp et al [44] claim that retrieval effectiveness is improved when utilizing contextual information. The approach of Hangal et al is limited by the fact that it can only analyze the sentiment of the text and does not take into account the affective state of the reader when receiving the message.…”

Section: B New Applications 1) Affective Metadata Taggingmentioning

confidence: 99%

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Ayzenberg

Picard

2014

IEEE J. Biomed. Health Inform.

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Abstract-The wide availability of low-cost wearable biophysiological sensors enables us to measure how the environment and our experiences impact our physiology. This creates a challenge: in order to interpret the longitudinal data, we require the matching contextual information as well. Collecting continuous biophysiological data makes it unfeasible to rely solely on our memory for contextual information. In this paper, we first present an architecture and implementation of a system for the acquisition, processing, and visualization of biophysiological signals and contextual information. Next, we present the results of a user study: users wore electrodermal activity wrist sensors that measured their autonomic arousal. These users uploaded the sensor data at the end of each day. At first, they annotated their events at the end of each day; then, after a two-day break, they annotated the data from two days earlier. One group of users had access to both the signal and the contextual information collected by the mobile phone and the other group could only access the biophysiological signal. At the end of the study, the users filled in a system usability scale and user experience surveys. Our results show that the system enables the users to annotate biophysiological signals at a greater effectiveness than the current state of the art while also providing very good usability.

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“…This collection is particularly valuable because the emails have been manually tagged so that the performance of different techniques can be measured. In Weerkamp et al (2009) this collection is used to improve search in email archives by using contextual information. The authors use thread structure of the emails to get better search results.…”

Section: Related Workmentioning

confidence: 99%

Network-based filtering for large email collections in E-Discovery

Henseler

2010

Artif Intell Law

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The information overload in E-Discovery proceedings makes reviewing expensive and it increases the risk of failure to produce results on time and consistently. New interactive techniques have been introduced to increase reviewer productivity. In contrast, the techniques presented in this article propose an alternative method that tries to reduce information during culling so that less information needs to be reviewed. The proposed method first focuses on mapping the email collection universe using straightforward statistical methods based on keyword filtering combined with date time and custodian identities. Subsequently, a social network is constructed from the email collection that is analyzed by filtering on date time and keywords. By using the network context we expect to provide a better understanding of the keyword hits and the ability to discard certain parts of the collection.

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Using Contextual Information to Improve Search in Email Archives

Cited by 16 publications

References 20 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Network-based filtering for large email collections in E-Discovery

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