  Entity linking (EL) for the rapidly growing short text (e.g. search queries
and news titles) is critical to industrial applications. Most existing
approaches relying on adequate context for long text EL are not effective for
the concise and sparse short text. In this paper, we propose a novel framework
called Multi-turn Multiple-choice Machine reading comprehension (M3}) to solve
the short text EL from a new perspective: a query is generated for each
ambiguous mention exploiting its surrounding context, and an option selection
module is employed to identify the golden entity from candidates using the
query. In this way, M3 framework sufficiently interacts limited context with
candidate entities during the encoding process, as well as implicitly considers
the dissimilarities inside the candidate bunch in the selection stage. In
addition, we design a two-stage verifier incorporated into M3 to address the
commonly existed unlinkable problem in short text. To further consider the
topical coherence and interdependence among referred entities, M3 leverages a
multi-turn fashion to deal with mentions in a sequence manner by retrospecting
historical cues. Evaluation shows that our M3 framework achieves the
state-of-the-art performance on five Chinese and English datasets for the
real-world short text EL.
