Entity Linking and Knowledge Graph Construction › Inspecting Alias Ambiguity

An alias dictionary can reveal ambiguity before a model is trained: inspect how many entities each string retrieves and how its anchor counts are distributed. Frequency and ambiguity are different properties. A rare name may have few training examples and still collide with another entity; a frequent name may have one dominant referent. Analyze both the number of candidates and the amount of supervision for the target entity.

The most frequent sense (MFS) baseline chooses the entity with the largest training-corpus anchor count for an alias. It exposes how much accuracy a system obtains from the alias prior alone. Its strength depends on the distribution of senses in the test set. In the toy table below, the company receives more apple counts than the fruit; those numbers are invented, not a measurement of Wikipedia anchors.

MFS is wrong whenever the current referent is outside the set of highest-count candidates. Words such as metal can support the element reading of Mercury, while Chicago Bulls can support Michael Jordan. Domain labels alone do not determine either answer: a chemistry article can discuss a planet, and a sports article can discuss a country. This is the same tension between a corpus prior and local evidence discussed in Lexical Semantics and Word Senses.

The prior estimates alias usage in its reference corpus; context supplies evidence about this occurrence. Neither establishes truth by itself. Compare a prior-only baseline with contextual scoring on development data from the intended domain, and inspect errors on rare entities separately. Botha et al. (2020) report challenges for rare entities and low-resource languages in their multilingual setting.