ca59afaf59560937af72be3e32b23ecad93dceff
Šeleng
Šeleng
Martin
Martin Šeleng
Martin
Martin Šeleng
Martin Šeleng
Simon
Simon
ab6d7f6be0a4f94d5084190f850196fb50c3134a
Simon Scerri
Scerri
Simon Scerri
Scerri
Simon Scerri
Alexander
Alexander Troussov
Troussov
Alexander
Alexander Troussov
Alexander Troussov
7e65d0bd72c96f4d71bc8dfe011bc9b2c0988821
Troussov
Štefan Dlugolinský
Dlugolinský
Štefan
Štefan Dlugolinský
1053c585cde81cc12895bbeaa7513def287fa203
Štefan
Štefan Dlugolinský
Dlugolinský
University of Sheffield
University of Sheffield
University of Sheffield
Digital Enterprise Research Institute (DERI), National University of Ireland, Galway
Digital Enterprise Research Institute (DERI), National University of Ireland, Galway
Digital Enterprise Research Institute (DERI), National University of Ireland, Galway
Valentina Presutti
Valentina
Presutti
Romain Vuillemot
45630f1c0abee11d5a3b720812c76b32060d474c
Romain Vuillemot
Romain Vuillemot
Romain
Vuillemot
Vuillemot
Romain
Riss
Uwe Riss
Uwe
Riss
Uwe Riss
4a7268a0ba6ceee57d7f2f9c08716343e8654694
Uwe Riss
Uwe
IBM
IBM
IBM
Microsoft Research
Microsoft Research
Microsoft Research
Anna Lisa Gentile
Anna Lisa
Gentile
Andrea Giovanni Nuzzolese
Andrea Giovanni
Nuzzolese
Dataset about email2012-alignments.
Tue May 03 19:03:50 CEST 2016
Microsoft
Microsoft
Microsoft
Aldo Gangemi
Aldo
Gangemi
PARC
PARC
PARC
David Ascher
70ab5be7469ee3777c21ad2ca3aacf988ce1d7cf
David Ascher
David
David Ascher
Ascher
David
Ascher
Palantir Technologies
Palantir Technologies
Palantir Technologies
FIIT STU in Bratislava
FIIT STU in Bratislava
FIIT STU in Bratislava
John Tang
John
Tang
John
John Tang
Tang
6a49f4166be469b58a5454399db1ca4bdc757ceb
John Tang
Thomas
Thomas Burkhart
Burkhart
Thomas
Thomas Burkhart
Thomas Burkhart
Burkhart
2d902abb05913ee4c30cb433867fc15bae90f5f8
text relevance
text relevance
Email search
Full-text search in email archives using social evaluation, attached and linked resources
Full-text search in email archives using social evaluation, attached and linked resources
social network
social relevance
attachment
fulltext search
social network
linked content
Full-text search in email archives using social evaluation, attached and linked resources
Emails are important tools for communication and cooperation, they contain large amount of information and connections to knowledge and data sources. Because of this, it is very important to improve the efficiency of their processing. This paper describes an email search system which integrates full-text search with social search while processing also the attached and linked resources.
Emails are important tools for communication and cooperation, they contain large amount of information and connections to knowledge and data sources. Because of this, it is very important to improve the efficiency of their processing. This paper describes an email search system which integrates full-text search with social search while processing also the attached and linked resources.
fulltext search
attachment
linked content
Email search
social relevance
LIRIS
LIRIS
LIRIS
SAP AG, SAP Research
SAP AG, SAP Research
SAP AG, SAP Research
Diana
Diana
Maynard
Diana Maynard
6a9054d3a369f74a6338c546dceceeeb7be8ed6a
Diana Maynard
Maynard
Diana Maynard
Institute of Informatics, Slovak Academy of Sciences
Institute of Informatics, Slovak Academy of Sciences
Institute of Informatics, Slovak Academy of Sciences
Laclavík
Michal Laclavík
Laclavík
Laclavik
Michal Laclavik
Michal
Michal Laclavik
Michal Laclavík
Michal
Laclavik
Michal Laclavik
Michal Laclavík
d9a209c6636a3031f57db42773665048d943e676
DFKI
DFKI
DFKI
Marek
Marek Ciglan
Ciglan
Marek
Marek Ciglan
Ciglan
be9034c4d072bc25bb7749e910a93ce4d4c52325
Marek Ciglan
everbread
everbread
everbread
named entities recognition
error detection
dynamic knowledge model correction
named entities recognition
sig block
error detection
contact details
UIMA
dynamic knowledge model correction
sig block
Interpreting Contact Details out of E-mail Signature Blocks
address book
NLP
contact details
This paper describes a fully automated process of address book enrichment by means of information extraction in e-mail signature blocks. The main issues we tackle are signature block detection, named entites tagging, mapping with a specific person, standardizing the details and auto-updating of the address book. We describe how the process was designed to handle multiple-type of errors (human or computer-driven) while aiming at 100% precision rate. Last, we tackle the question of automatic updating confronted to users rights over their own data.
Interpreting Contact Details out of E-mail Signature Blocks
NLP
This paper describes a fully automated process of address book enrichment by means of information extraction in e-mail signature blocks. The main issues we tackle are signature block detection, named entites tagging, mapping with a specific person, standardizing the details and auto-updating of the address book. We describe how the process was designed to handle multiple-type of errors (human or computer-driven) while aiming at 100% precision rate. Last, we tackle the question of automatic updating confronted to users rights over their own data.
UIMA
address book
Interpreting Contact Details out of E-mail Signature Blocks
email signature
email signature
Gaëlle Recourcé
Gaëlle
fdbae6d3546578ad1063451bdc5e41ba7b0e66c4
Recourcé
Gaëlle
Recourcé
Gaëlle Recourcé
Gaëlle Recourcé
Hluchy
Ladislav
Ladislav Hluchy
Hluchy
Ladislav Hluchy
Ladislav Hluchy
01834214864d4c8207caef9190bd9fd13570e056
Ladislav
Vojtech Juhasz
Vojtech
Vojtech
Juhasz
Juhasz
1b83683b8202865f2fca1e620035c127e18ef3ac
Vojtech Juhasz
Vojtech Juhasz
(unknown)
(unknown)
(unknown)
Smith
Ian Smith
Ian Smith
Ian
29e8f19284c58c2adc1919c403f738f3ea4ae93e
Smith
Ian
Ian Smith
Emails as Graph: Relation Discovery in Email Archive
Enron corpus
Emails as Graph: Relation Discovery in Email Archive
relation discovery
Enron corpus
social networks
In this paper, we present an approach for representing an email archive in form of a network, capturing the communication among users and relations between entities extracted from the textual part of the email messages. We showcase the method on the Enron email corpus, from which we extract various entities and a social network. Extracted entities are organized in a graph including email connected with named entities (NE) extracted from emails such as people, email addresses, telephone numbers. Edges in the graph denote relations between NEs, representing occurrence in same email part, paragraph, sentence or composite NE. We study mathematical properties of the graph structure created by the proposed approach and we describe our hands-on experience with the processing of such structure. Enron Graph corpus contains a few millions of nodes and it is a large corpus for experimenting with various graph-querying techniques, e.g. graph traversing or spread of activation. Due to its size, the exploitation of traditional graph processing libraries might be problematic as that keep the whole structure in the memory. We describe our experience with the management of such data and with the relation discovery among extracted entities. The described experience might be valuable for practitioners and highlights several research challenges.
social networks
search
search
graph data
In this paper, we present an approach for representing an email archive in form of a network, capturing the communication among users and relations between entities extracted from the textual part of the email messages. We showcase the method on the Enron email corpus, from which we extract various entities and a social network. Extracted entities are organized in a graph including email connected with named entities (NE) extracted from emails such as people, email addresses, telephone numbers. Edges in the graph denote relations between NEs, representing occurrence in same email part, paragraph, sentence or composite NE. We study mathematical properties of the graph structure created by the proposed approach and we describe our hands-on experience with the processing of such structure. Enron Graph corpus contains a few millions of nodes and it is a large corpus for experimenting with various graph-querying techniques, e.g. graph traversing or spread of activation. Due to its size, the exploitation of traditional graph processing libraries might be problematic as that keep the whole structure in the memory. We describe our experience with the management of such data and with the relation discovery among extracted entities. The described experience might be valuable for practitioners and highlights several research challenges.
email
graph data
relation discovery
email
Emails as Graph: Relation Discovery in Email Archive
Ducheneaut
Ducheneaut
Nicolas Ducheneaut
53e39c4660987bb47211a9856c8ab07bf7bf4637
Nicolas Ducheneaut
Nicolas
Nicolas
Nicolas Ducheneaut
Vitor Carvalho
Vitor
Carvalho
Vitor Carvalho
d7fe7398093d6a760c8fe5f63d65086380b7dd86
Vitor Carvalho
Vitor
Carvalho
Institute of Informatics SAS
Institute of Informatics SAS
Institute of Informatics SAS
Kwaga
kwaga
kwaga
kwaga
Kwaga
Kwaga
Andrew Lampert
Andrew
5e4fc3bd68f95b14b29679925410e747b26a057a
Andrew
Andrew Lampert
Andrew Lampert
Lampert
Lampert