The Journal of Machine Learning Research
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Exploring content models for multi-document summarization
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
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
A generative entity-mention model for linking entities with knowledge base
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Knowledge base population: successful approaches and challenges
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
Collective entity linking in web text: a graph-based method
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Entity disambiguation with hierarchical topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative ranking: a case study on entity linking
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Robust disambiguation of named entities in text
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Linking entities to a knowledge base with query expansion
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
LINDEN: linking named entities with knowledge base via semantic knowledge
Proceedings of the 21st international conference on World Wide Web
Collective context-aware topic models for entity disambiguation
Proceedings of the 21st international conference on World Wide Web
Entity linking with effective acronym expansion, instance selection and topic modeling
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
An entity-topic model for entity linking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Discovering emerging entities with ambiguous names
Proceedings of the 23rd international conference on World wide web
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Named entity disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a knowledge base such as Wikipedia. Such disambiguation can help enhance readability and add semantics to plain text. It is also a central step in constructing high-quality information network or knowledge graph from unstructured text. Previous research has tackled this problem by making use of various textual and structural features from a knowledge base. Most of the proposed algorithms assume that a knowledge base can provide enough explicit and useful information to help disambiguate a mention to the right entity. However, the existing knowledge bases are rarely complete (likely will never be), thus leading to poor performance on short queries with not well-known contexts. In such cases, we need to collect additional evidences scattered in internal and external corpus to augment the knowledge bases and enhance their disambiguation power. In this work, we propose a generative model and an incremental algorithm to automatically mine useful evidences across documents. With a specific modeling of "background topic" and "unknown entities", our model is able to harvest useful evidences out of noisy information. Experimental results show that our proposed method outperforms the state-of-the-art approaches significantly: boosting the disambiguation accuracy from 43% (baseline) to 86% on short queries derived from tweets.