FINET: Context-Aware Fine-Grained Named Entity Typing
Luciano Del Corro, Abdalghani Abujabal, Rainer Gemulla, Gerhard Weikum · 2015
We propose FINET, a system for detecting the types of named entities in short inputs-such as sentences or tweets-with respect to WordNet's super fine-grained type system.FINET generates candidate types using a sequence of multiple extractors, ranging from explicitly mentioned types to implicit types, and subsequently selects the most appropriate using ideas from word-sense disambiguation.FINET combats data scarcity and noise from existing systems: It does not rely on supervision in its extractors and generates training data for type selection from WordNet and other resources.FINET supports the most fine-grained type system so far, including types with no annotated training data.Our experiments indicate that FINET outperforms state-of-the-art methods in terms of recall, precision, and granularity of extracted types.