Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis. For example, they allow us to find documents relevant to a specific entity irrespective of the underlying syntactic expression within a document. However, the entities that are commonly represented in knowledge bases are often a simplification of what is truly being referred to in text. For example, in a knowledge base, we may have an entity for Germany as a country but not for the more fuzzy concept of Germany that covers notions of German Population, German Drivers, and the German Government. Inspired by recent advances in contextual word embeddings, we introduce the concept of entity spaces - specific representations of a set of associated entities with near-identity. Thus, these entity spaces provide a handle to an amorphous grouping of entities. We developed a proof-of-concept for English showing how, through the introduction of entity spaces in the form of disambiguation pages, the recall of entity linking can be improved.
|Title of host publication||LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings|
|Publisher||European Language Resources Association (ELRA)|
|Number of pages||9|
|Publication status||Published - Jun 2020|
|Name||LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings|
- Entity linking
- Knowledge representation