Wikification and Beyond: The Challenges of Entity and Concept Grounding

Contextual disambiguation and grounding of concepts and entities in natural language are essential to progress in many natural language understanding tasks and fundamental to many applications. Wikification aims at automatically identifying concept mentions in text and linking them to referents in a knowledge base (KB) (e.g., Wikipedia). Consider the sentence, "The Times report on Blumenthal (D) has the potential to fundamentally reshape the contest in the Nutmeg State.". A Wikifier should identify the key entities and concepts and map them to an encyclopedic resource (e.g., “D” refers to Democratic Party, and “the Nutmeg State” refers to Connecticut. Wikification benefits end-users and Natural Language Processing (NLP) systems. Readers can better comprehend Wikified documents as information about related topics is readily accessible. For systems, a Wikified document elucidates concepts and entities by grounding them in an encyclopedic resource or an ontology. Wikification output has improved NLP down-stream tasks, including coreference resolution, user interest discovery , recommendation and search. This task has received increased attention in recent years from the NLP and Data Mining communities, partly fostered by the U.S. NIST Text Analysis Conference Knowledge Base Population (KBP) track, and several versions of it has been studied. These include Wikifying all concept mentions in a single text document; Wikifying a cluster of co-referential named entity mentions that appear across documents (Entity Linking), and Wikifying a whole document to a single concept. Other works relate this task to coreference resolution within and across documents and in the context of multiple text genres. 2 Content Overview

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