Foundations of statistical natural language processing
Foundations of statistical natural language processing
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Combining association measures for collocation extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Annotating and recognising named entities in clinical notes
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
An overview of Microsoft web N-gram corpus and applications
HLT-DEMO '10 Proceedings of the NAACL HLT 2010 Demonstration Session
Annotating named entities in Twitter data with crowdsourcing
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
A generalized method for word sense disambiguation based on wikipedia
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Recognizing named entities in tweets
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Lexical normalisation of short text messages: makn sens a #twitter
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Part-of-speech tagging for Twitter: annotation, features, and experiments
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Named entity recognition in tweets: an experimental study
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Twevent: segment-based event detection from tweets
Proceedings of the 21st ACM international conference on Information and knowledge management
Community-based classification of noun phrases in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploiting hybrid contexts for Tweet segmentation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Linking named entities in Tweets with knowledge base via user interest modeling
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
FS-NER: a lightweight filter-stream approach to named entity recognition on twitter data
Proceedings of the 22nd international conference on World Wide Web companion
Location extraction from disaster-related microblogs
Proceedings of the 22nd international conference on World Wide Web companion
RESLVE: leveraging user interest to improve entity disambiguation on short text
Proceedings of the 22nd international conference on World Wide Web companion
Effective named entity recognition for idiosyncratic web collections
Proceedings of the 23rd international conference on World wide web
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Many private and/or public organizations have been reported to create and monitor targeted Twitter streams to collect and understand users' opinions about the organizations. Targeted Twitter stream is usually constructed by filtering tweets with user-defined selection criteria e.g. tweets published by users from a selected region, or tweets that match one or more predefined keywords. Targeted Twitter stream is then monitored to collect and understand users' opinions about the organizations. There is an emerging need for early crisis detection and response with such target stream. Such applications require a good named entity recognition (NER) system for Twitter, which is able to automatically discover emerging named entities that is potentially linked to the crisis. In this paper, we present a novel 2-step unsupervised NER system for targeted Twitter stream, called TwiNER. In the first step, it leverages on the global context obtained from Wikipedia and Web N-Gram corpus to partition tweets into valid segments (phrases) using a dynamic programming algorithm. Each such tweet segment is a candidate named entity. It is observed that the named entities in the targeted stream usually exhibit a gregarious property, due to the way the targeted stream is constructed. In the second step, TwiNER constructs a random walk model to exploit the gregarious property in the local context derived from the Twitter stream. The highly-ranked segments have a higher chance of being true named entities. We evaluated TwiNER on two sets of real-life tweets simulating two targeted streams. Evaluated using labeled ground truth, TwiNER achieves comparable performance as with conventional approaches in both streams. Various settings of TwiNER have also been examined to verify our global context + local context combo idea.