Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From information to knowledge: harvesting entities and relationships from web sources
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Adapting boosting for information retrieval measures
Information Retrieval
Ranking related entities: components and analyses
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
Ranking Entity Facets Based on User Click Feedback
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Jigs and lures: associating web queries with structured entities
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
Robust disambiguation of named entities in text
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Compressed data structures for annotated web search
Proceedings of the 21st international conference on World Wide Web
LINDEN: linking named entities with knowledge base via semantic knowledge
Proceedings of the 21st international conference on World Wide Web
KORE: keyphrase overlap relatedness for entity disambiguation
Proceedings of the 21st ACM international conference on Information and knowledge management
When entities meet query recommender systems: semantic search shortcuts
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Dexter: an open source framework for entity linking
Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval
Dexter: an open source framework for entity linking
Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval
Twitter anticipates bursts of requests for Wikipedia articles
Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
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Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowledge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant entities selected for annotation, since this minimizes errors in disambiguating entity-linking. The definition of an effective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of different state-of-the-art entity-linking algorithms.