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Entity linking (EL) for the rapidly growing short text (e.g. search queriesand news titles) is critical to industrial applications. Most existingapproaches relying on adequate context for long text EL are not effective forthe concise and sparse short text. In this paper, we propose a novel frameworkcalled Multi-turn Multiple-choice Machine reading comprehension (M3}) to solvethe short text EL from a new perspective: a query is generated for eachambiguous mention exploiting its surrounding context, and an option selectionmodule is employed to identify the golden entity from candidates using thequery. In this way, M3 framework sufficiently interacts limited context withcandidate entities during the encoding process, as well as implicitly considersthe dissimilarities inside the candidate bunch in the selection stage. Inaddition, we design a two-stage verifier incorporated into M3 to address thecommonly existed unlinkable problem in short text. To further consider thetopical coherence and interdependence among referred entities, M3 leverages amulti-turn fashion to deal with mentions in a sequence manner by retrospectinghistorical cues. Evaluation shows that our M3 framework achieves thestate-of-the-art performance on five Chinese and English datasets for thereal-world short text EL.

Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking