Detecting and Browsing Events in Unstructured text
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Visualizing Sequential Patterns for Text Mining
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph-based ranking algorithms for sentence extraction, applied to text summarization
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Novelty detection: the TREC experience
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A translation model for sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Text Clustering Algorithm Based on Lexical Graph
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
'Show me more': Incremental length summarisation using novelty detection
Information Processing and Management: an International Journal
TSCAN: a novel method for topic summarization and content anatomy
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Coreex: content extraction from online news articles
Proceedings of the 17th ACM conference on Information and knowledge management
Lexical Graphs for Improved Contextual Ad Recommendation
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual analysis of documents with semantic graphs
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Constructing Event Templates from Written News
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Discovery of interactive graphs for understanding and searching time-indexed corpora
Knowledge and Information Systems
Connecting the dots between news articles
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
From bursty patterns to bursty facts: The effectiveness of temporal text mining for news
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Experience STORIES: a visual news search and summarization system
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Discovering emerging topics in unlabelled text collections
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
| Hi-index | 0.00 |
With the growing number of document sets accessible online, tracking their evolution over time story tracking became an increasingly interesting problem. In this paper we propose a story tracking method based on the dynamics of keyword-association graphs. We create a graph representation of the story evolution that we call story graphs, and investigate how graph structure can be used for detecting and discovering new developments in the story. First we investigate the possibly interesting graph properties for development detection. We continue by investigating how graph structure can be linked to the sentences representing developments. For this we create an evaluation framework which bridges the gap between temporal text mining patterns and sentences. We apply this framework to evaluate our method against other temporal text mining methods. Our experiments show that story graphs perform at similar levels overall, but provide distinctive advantages in some settings.