Semantic language models for topic detection and tracking

Authors:
Ramesh Nallapati
Affiliations:
University of Massachusetts, Amherst, MA
Venue:
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Year:
2003

Citing 9
Cited 7

A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An Algorithm that Learns What‘s in a Name

Machine Learning - Special issue on natural language learning
A vector space model for automatic indexing

Communications of the ACM
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Learning Approaches for Detecting and Tracking News Events

IEEE Intelligent Systems
Introduction to topic detection and tracking

Topic detection and tracking
Word Semantics for Information Retrieval: Moving One Step Closer to the Semantic Web

ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
The Design and Implementation of a Part of Speech Tagger for English

The Design and Implementation of a Part of Speech Tagger for English
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Automatic discovery of technology trends from patent text

Proceedings of the 2009 ACM symposium on Applied Computing
Story link detection based on event model with uneven SVM

AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Two-tier similarity model for story link detection

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Story link detection based on event words

CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Fine-grained topic detection in news search results

Proceedings of the 27th Annual ACM Symposium on Applied Computing
Unsupervised and supervised learning to evaluate event relatedness based on content mining from social-media streams

Expert Systems with Applications: An International Journal
Learning to explore spatio-temporal impacts for event evaluation on social media

ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we present a new semantic language modeling approach to model news stories in the Topic Detection and Tracking (TDT) task. In the new approach, we build a unigram language model for each semantic class in a news story. We also cast the link detection subtask of TDT as a two-class classification problem in which the features of each sample consist of the generative log-likelihood ratios from each semantic class. We then compute a linear discriminant classifier using the perceptron learning algorithm on the training set. Results on the test set show a marginal improvement over the unigram performance, but are not very encouraging on the whole.