Instantiation

TL;DR

This paper investigates the linguistic and computational modeling of instantiation relations between entities and categories, introducing a new dataset and analyzing distributional properties to improve detection methods.

Contribution

It introduces a novel dataset for instantiation detection and analyzes distributional properties of entities and categories to enhance modeling approaches.

Findings

Entities form regions in distributional space.

Category embeddings are often outside entity regions.

Using entity-based category representations improves instantiation detection.

Abstract

In computational linguistics, a large body of work exists on distributed modeling of lexical relations, focussing largely on lexical relations such as hypernymy (scientist -- person) that hold between two categories, as expressed by common nouns. In contrast, computational linguistics has paid little attention to entities denoted by proper nouns (Marie Curie, Mumbai, ...). These have investigated in detail by the Knowledge Representation and Semantic Web communities, but generally not with regard to their linguistic properties. Our paper closes this gap by investigating and modeling the lexical relation of instantiation, which holds between an entity-denoting and a category-denoting expression (Marie Curie -- scientist or Mumbai -- city). We present a new, principled dataset for the task of instantiation detection as well as experiments and analyses on this dataset. We obtain the following results: (a), entities belonging to one category form a region in distributional space, but the embedding for the category word is typically located outside this subspace; (b) it is easy to learn to distinguish entities from categories from distributional evidence, but due to (a), instantiation proper is much harder to learn when using common nouns as representations of categories; (c) this problem can be alleviated by using category representations based on entity rather than category word embeddings.