A New Email Retrieval Ranking Approach

Email Retrieval task has recently taken much attention to help the user retrieve the email(s) related to the submitted query. Up to our knowledge, existing email retrieval ranking approaches sort the retrieved emails based on some heuristic rules, which are either search clues or some predefined user criteria rooted in email fields. Unfortunately, the user usually does not know the effective rule that acquires best ranking related to his query. This paper presents a new email retrieval ranking approach to tackle this problem. It ranks the retrieved emails based on a scoring function that depends on crucial email fields, namely subject, content, and sender. The paper also proposes an architecture to allow every user in a network/group of users to be able, if permissible, to know the most important network senders who are interested in his submitted query words. The experimental evaluation on Enron corpus prove that our approach outperforms known email retrieval ranking approachesComment: 20 page

Finding Relevant Answers in Software Forums

Abstract—Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containin

A system to support dissemination of knowledge and sharing of experiences in the working environment

In the information era enterprises strive to be productive and efficient. One feature of this goal is to engage their employees in education programmes, help them gain new experiences and knowledge and adapt to an ever-changing working environment. Such programmes require thorough design in order to achieve satisfactory results. Lately, enterprises recognising the role technology can play in the education of their employees, have adopted systems that supplement the traditional educational model with mechanisms that enable the sharing of experiences and knowledge [5]. In this paper we describe an architecture and a system prototype that allows users to search easily for information, interact with colleagues and share experiences, to compose and disseminate best practices and knowledge. The design of this system is based on insights gained from the operation of the Greek Taxation System

Email Thread Reassembly Using Similarity Matching

Email thread reassembly is the task of linking messages by parent-child relationships. In this paper, we present two approaches to address this problem. One exploits previously undocumented header information from the Microsoft Exchange Protocol. The other uses string similarity metrics and a heuristic algorithm to reassemble threads in the absence of header information. The pros and cons of both methods are discussed. The similarity matching method is evaluated using the Enron email corpus and found to perform well

Discovery-led refinement in e-discovery investigations: sensemaking, cognitive ergonomics and system design.

Given the very large numbers of documents involved in e-discovery investigations, lawyers face a considerable challenge of collaborative sensemaking. We report findings from three workplace studies which looked at different aspects of how this challenge was met. From a sociotechnical perspective, the studies aimed to understand how investigators collectively and individually worked with information to support sensemaking and decision making. Here, we focus on discovery-led refinement; specifically, how engaging with the materials of the investigations led to discoveries that supported refinement of the problems and new strategies for addressing them. These refinements were essential for tractability. We begin with observations which show how new lines of enquiry were recursively embedded. We then analyse the conceptual structure of a line of enquiry and consider how reflecting this in e-discovery support systems might support scalability and group collaboration. We then focus on the individual activity of manual document review where refinement corresponded with the inductive identification of classes of irrelevant and relevant documents within a collection. Our observations point to the effects of priming on dealing with these efficiently and to issues of cognitive ergonomics at the human–computer interface. We use these observations to introduce visualisations that might enable reviewers to deal with such refinements more efficiently

Window based Enterprise Expert Search

This is the first year for the participation of the City University Centre of Interactive System Research (CISR) in the Expert Search Task. In this paper, we describe an expert search experiment based on window-based techniques, that is, we build profile for each expert by using information around the expert’s name and email address in the documents. We then use the traditional IR techniques to search and rank experts. Our experiment is done on Okapi and BM25 is used as the ranking model. Results show that parameter b does have an effect on the retrieval effectiveness and using a smaller value for b produces better results

Clustering and Classification of Email Contents

Information users depend heavily on emails\u27 system as one of the major sources of communication. Its importance and usage are continuously growing despite the evolution of mobile applications, social networks, etc. Emails are used on both the personal and professional levels. They can be considered as official documents in communication among users. Emails\u27 data mining and analysis can be conducted for several purposes such as: Spam detection and classification, subject classification, etc. In this paper, a large set of personal emails is used for the purpose of folder and subject classifications. Algorithms are developed to perform clustering and classification for this large text collection. Classification based on NGram is shown to be the best for such large text collection especially as text is Bi-language (i.e. with English and Arabic content)

A Visual Framework for Graph and Text Analytics in Email Investigation

The aim of this work is to build a framework which can benefit from data analysis techniques to explore and mine important information stored in an email collection archive. The analysis of email data could be accomplished from different perspectives, we mainly focused our approach on two different aspects: social behaviors and the textual content of the emails body. We will present a review on the past techniques and features adopted to handle this type of analysis, and evaluate them in real tools. This background will motivate our choices and proposed approach, and help us build a final visual framework which can analyze and show social graph networks along with other data visualization elements that assist users in understanding and dynamically elaborating the email data uploaded. We will present the architecture and logical structure of the framework, and show the flexibility nature of the system for future integrations and improvements. The functional aspects of our approach will be tested using the ‘enron dataset’, and by applying real key actors involved in the ‘enron case’ scandal