Entity Linking and Knowledge Graph Construction › Mention Detection for Linking

An NER model from Named Entity Recognition can supply candidate spans, but its annotation inventory may differ from the linking task's inventory. A linker that must identify nominal concepts, for example, needs spans that a proper-name tagger may never propose. Check the target annotation policy before reusing the detector unchanged.

The desired mention inventory depends on the linking task. It may include:

• Nominal mentions: "the company", "the tech giant", "the city".
• Nested and overlapping spans: in Bank of America Tower, the building and the bank are different entities.
• Targets outside a particular detector's inventory, possibly including works of art, laws, proteins, chemical compounds, or events.

For example, resolving "the company" can require discourse information beyond a name dictionary. The string Bank of America Tower contains an organization name, but whether to annotate both spans is a dataset decision, not an automatic requirement. Biomedical concept linking can use a different inventory again; MedMentions annotates mentions of UMLS concepts in biomedical abstracts (Mohan and Li, 2019).

One design is to propose spans at high recall and use later scoring to reject unsupported proposals. Scoring more proposals adds work when cost per proposal is fixed and positive; high detection recall alone does not guarantee precision after filtering. Keep two rejection reasons separate: a span may not be an entity mention at all, or it may be a valid mention whose referent is outside the KB. Evaluate span rejection and NIL handling according to the task's annotation rules.

Joint training offers another design: use mention-entity compatibility to help decide which spans to retain. Kolitsas et al. (2018) learn scores useful to both detection and disambiguation. A separate detector can also use KB-aware features; the distinction is whether its decisions are trained jointly with the linker, not whether it can access such evidence at all.

In a dictionary-based linker, the table provides both candidates and a prior. Record the corpus and KB versions used to build it, resolve redirects consistently, and choose normalization rules that match the task. A dictionary is one candidate-generation design; description-based retrieval can operate without an anchor table, as in the zero-shot setting of Logeswaran et al. (2019).