Time-Aware and Corpus-Specific Entity Relatedness
This work addresses the need for dynamic entity relatedness in applications like information retrieval and entity linking, though it appears incremental as it builds on existing embedding methods.
The authors tackled the problem of entity relatedness varying with time and corpus context by proposing a simple, flexible model that uses time-aware and corpus-specific word embeddings, achieving language independence and eliminating the need for external knowledge.
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but flexible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.