Retrieval of discussions from enterprise mailing lists

Kolla, Maheedhar; Vechtomova, Olga

doi:10.1145/1277741.1277956

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The identified thread emails can be further used to form thread clusters. Thread emails are used for various purposes, conversation thread detection are useful to detect discussion in mailing list [13], topic detection [33], email summarization [9], email classification, question answering, [18] [19], visualization [15] [17]. Thread grouping enhance awareness of others' contributions on a topic, and minimize lost messages by clustering related e-mail [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Email Thread Identification and Management

Kulkarni,

Acharya

2019

IJEAT

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Nowadays, Email communication is use as primary communication tool in the business domain as well as in education sector. Due to massive incoming emails, overflowinginbox is one of the problems faced by email users. There are several reasons for such a situation, one of them being the unnecessary mass of thread emails. They are retained in inbox even when they are not necessary. Even if this email is deleted from inbox, the next message as thread email will hit your inbox. Wrong use of "reply-all" tab adds to this situation called "Email storm". Thread emails are often generated because of users" careless habit to click on "Replyall" button. It is almost like a reflex action on their part. This work intends to solve the problem of email storm on two fronts :  Identification of thread emails  Automatically controlling thread email The three datasets Din, Dadm and Dexam from academic domain are used as training data. The experimental outcome shows that "In-Reply-To", "References" and additionally "threadindex" are the dominant features in identifying thread emails. We have used these features to derive thread classification strategy. The developed algorithm is used to test four datasets D cor , D CS , D F1 and D F2 . Using this method accuracy upto 99.91% is achieved. Further, the paper also suggests access control rights strategy to control email storm. The model is proposed for controlling thread emails in education domain. The control mechanism will help system administrators to control email traffic.

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Section: Literature Reviewmentioning

confidence: 99%

Email Thread Identification and Management

Kulkarni,

Acharya

2019

IJEAT

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“…Hawking [11] discussed several challenges in enterprise search, but it remains unclear what are the effective strategies to improve search accuracy. The enterprise track in TREC [7,4] has attracted more research efforts on improving enterprise search quality including expert finding [1,19] and document search [12,16]. However, to our best knowledge, few work has been done on utilizing entities to improve enterprise search.…”

Section: Related Workmentioning

confidence: 99%

Entity centric query expansion for enterprise search

Liu

Fang

Chen

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Many information needs of enterprise search center around entities. Intuitively, information related to the entities mentioned in the query, such as related entities, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are termcentric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities to improve search quality. Experiment results show that the proposed entity-centric query expansion strategy is more effective to improve the search performance than the state-of-the-art pseudo feedback methods on longer, natural language-like queries with entities.

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“…A conversation thread in an email corpus is formally defined as a set of exchanged emails on the same topic among the same group of people by reply or forwarding [1]. Recently, conversation thread detection from email data has been profitably employed in several applications, like discussion search in public mailing lists [2][3][4][5][6], topic detection in email corpora [7], email summarization [8,9], email classification [10,11], question answering [12,13], expert finding [14,15], improvement of user experience in email management [16], visualization [17,18], analysis of information sharing in interactive environments [19] and detection of argument patterns to evaluate the creditability of messages in conversations [20].…”

Section: Introductionmentioning

confidence: 99%

A learning approach for email conversation thread reconstruction

Dehghani

Shakery

Asadpour

et al. 2013

Journal of Information Science

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An email conversation thread is defined as a topic-centric discussion unit that is composed of exchanged emails among the same group of people by reply or forwarding. Detecting conversation threads contained in email corpora can be beneficial for both humans to digest the content of discussions and automatic methods to extract useful information from the conversations. This research explores two new feature-enriched learning approaches, LExLinC and LExTreC, to reconstruct linear structure and tree structure of conversation threads in email data. In this work, some simplifying assumptions considered in previous methods for extracting conversation threads are relaxed, which makes the proposed methods more powerful in detecting real conversations. Additionally, the supervised nature of the proposed methods makes them adaptable to new environments by automatically adjusting the features and their weights. Experimental results show that the proposed methods are highly effective in detecting conversation threads and outperform the existing methods.

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Retrieval of discussions from enterprise mailing lists

Cited by 5 publications

References 4 publications

Email Thread Identification and Management

Email Thread Identification and Management

Entity centric query expansion for enterprise search

A learning approach for email conversation thread reconstruction

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