Acquisition of categorized named entities for web search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Fine-grained named entity recognition and relation extraction for question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Collective annotation of Wikipedia entities in web text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Weakly-supervised acquisition of labeled class instances using graph random walks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Web-scale distributional similarity and entity set expansion
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Toward completeness in concept extraction and classification
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Experiments in graph-based semi-supervised learning methods for class-instance acquisition
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Entity disambiguation for knowledge base population
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Inducing fine-grained semantic classes via hierarchical and collective classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Coupled semi-supervised learning
Coupled semi-supervised learning
Exploring entity relations for named entity disambiguation
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Local and global algorithms for disambiguation to Wikipedia
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Understanding semantic change of words over centuries
Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web
Coupled temporal scoping of relational facts
Proceedings of the fifth ACM international conference on Web search and data mining
Class label enhancement via related instances
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Targeted disambiguation of ad-hoc, homogeneous sets of named entities
Proceedings of the 21st international conference on World Wide Web
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
ClausIE: clause-based open information extraction
Proceedings of the 22nd international conference on World Wide Web
Exploring re-ranking approaches for joint named-entityrecognition and linking
Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
Acquisition of open-domain classes via intersective semantics
Proceedings of the 23rd international conference on World wide web
Discovering emerging entities with ambiguous names
Proceedings of the 23rd international conference on World wide web
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Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we show that once the Wikipedia entities mentioned in a corpus of textual assertions are linked, this can further enable the detection and fine-grained typing of the unlinkable entities. Our proposed method for detecting unlinkable entities achieves 24% greater accuracy than a Named Entity Recognition baseline, and our method for fine-grained typing is able to propagate over 1,000 types from linked Wikipedia entities to unlinkable entities. Detection and typing of unlinkable entities can increase yield for NLP applications such as typed question answering.