Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Semantic computation in a Chinese question-answering system
Journal of Computer Science and Technology
Topic-conditioned novelty detection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Query based event extraction along a timeline
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Novelty detection based on sentence level patterns
Proceedings of the 14th ACM international conference on Information and knowledge management
Computation on sentence semantic distance for novelty detection
Journal of Computer Science and Technology
The nature of novelty detection
Information Retrieval
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
An information-pattern-based approach to novelty detection
Information Processing and Management: an International Journal
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Blended metrics for novel sentence mining
Expert Systems with Applications: An International Journal
Evaluation of novelty metrics for sentence-level novelty mining
Information Sciences: an International Journal
Database optimization for novelty detection
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Detecting novel business blogs
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Multilingual novelty detection
Expert Systems with Applications: An International Journal
An intelligent system for sentence retrieval and novelty mining
International Journal of Knowledge Engineering and Data Mining
Database optimization for novelty mining of business blogs
Expert Systems with Applications: An International Journal
ForAVis: explorative user forum analysis
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Dimensionality reduction for blog tag mining
International Journal of Web Engineering and Technology
Multilingual sentence categorization and novelty mining
Information Processing and Management: an International Journal
Chinese categorization and novelty mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
International Journal of Advanced Pervasive and Ubiquitous Computing
Probabilistic Models for Social Media Mining
International Journal of Information Technology and Web Engineering
Adaptable Services for Novelty Mining
International Journal of Systems and Service-Oriented Engineering
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Novel sentence detection aims at identifying novel information from an incoming stream of sentences. Our research applies named entity recognition (NER) and part-of-speech (POS) tagging on sentence-level novelty detection and proposes a mixed method to utilize these two techniques. Furthermore, we discuss the performance when setting different history sentence sets. Experimental results of different approaches on TREC'04 Novelty Track show that our new combined method outperforms some other novelty detection methods in terms of precision and recall. The experimental observations of each approach are also discussed.