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Entity linking is one of the problems to be handled in order to process natural language and to enrich the existing unstructured text with metadata. The generation of assignments between knowledge base entities and lexical units is called entity linking. Although a number of systems have been proposed for linking entity mentions in various languages, there is currently no publicly available entity linking system specific to the Turkish language. This paper presents a novel entity linking system—THINKER - for linking Turkish content with entities defined in the Turkish dictionary (tdk.gov.tr) or Turkish Wikipedia (tr.wikipedia.org). Specifically, we first propose a novel machine learning based entity detection algorithm for the Turkish language. Then, we propose a collective disambiguation algorithm which utilizes a set of metrics for the linking task and, which is optimized using a genetic algorithm. The effectiveness of THINKER is validated empirically over generated data sets. The experimental results show that THINKER outperformed the state-of-the-art cross-lingual and multilingual entity linking systems in the literature. High entity linking performance (74.81 percent F1 score) is achieved by extending previous methods with some features specific to Turkish language and by developing a novel method that can learn better representations of entity embeddings. [ABSTRACT FROM PUBLISHER]

Entity-linking systems link noun phrase mentions in a text to their corresponding knowledge base entities in order to enrich a text with metadata. Wikipedia is a popular and comprehensive knowledge base that is widely used in entity-linking systems. However, long-tail entities are not popular enough to have their own Wikipedia articles. Therefore, a knowledge base created by using Wikipedia entities would be limited to only popular entities. In order to overcome the knowledge base coverage limitation of Wikipedia-based entity-linking systems, this paper presents an entity-discovery system that can detect semantic types of entities that are not defined in Wikipedia. The effectiveness of the proposed system was validated empirically through the use of generated data sets for the Turkish language. The experimental results show that, in terms of accuracy, our system performs competitively in comparison to the previous methods in the literature. Its high performance is achieved through a method that learns word embeddings for candidate entities. [ABSTRACT FROM AUTHOR]