Entity-Linking via Graph-Distance Minimization
This work addresses entity-linking for natural language processing, but it is incremental as it builds on existing graph-based formalizations with new variants and heuristics.
The paper tackles the entity-linking problem by formalizing it as a graph optimization problem to minimize average distances between chosen items, proving NP-hardness but showing linear-time solvability under restrictive assumptions and proposing two heuristics that are experimentally evaluated against baselines on a real-world dataset.
Entity-linking is a natural-language-processing task that consists in identifying the entities mentioned in a piece of text, linking each to an appropriate item in some knowledge base; when the knowledge base is Wikipedia, the problem comes to be known as wikification (in this case, items are wikipedia articles). One instance of entity-linking can be formalized as an optimization problem on the underlying concept graph, where the quantity to be optimized is the average distance between chosen items. Inspired by this application, we define a new graph problem which is a natural variant of the Maximum Capacity Representative Set. We prove that our problem is NP-hard for general graphs; nonetheless, under some restrictive assumptions, it turns out to be solvable in linear time. For the general case, we propose two heuristics: one tries to enforce the above assumptions and another one is based on the notion of hitting distance; we show experimentally how these approaches perform with respect to some baselines on a real-world dataset.