English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Leveraging Semantic Annotations for Event-focused Search & Summarization

Mishra, A. (2018). Leveraging Semantic Annotations for Event-focused Search & Summarization. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-27108.

Item is

Files

show Files

Locators

show
hide
Description:
-
OA-Status:
Green

Creators

show
hide
 Creators:
Mishra, Arunav1, 2, Author           
Berberich, Klaus1, Advisor           
Weikum, Gerhard1, Referee           
Hauff, Claudia3, Referee
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, ou_1116551              
3External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.

Details

show
hide
Language(s): eng - English
 Dates: 20172018-03-122018-02-08
 Publication Status: Published online
 Pages: 252 p.
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Mishraphd2018
URN: urn:nbn:de:bsz:291-scidok-ds-271081
DOI: 10.22028/D291-27108
 Degree: PhD

Event

show

Legal Case

show

Project information

show

Source

show