Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities
This work addresses the problem of entity linking in microblogs for researchers and practitioners, offering an incremental improvement by incorporating social network information.
The paper tackles the challenge of entity linking in microblogs by leveraging social network structure to capture implicit context, resulting in F1 improvements of 1%-5% on benchmark datasets compared to the previous state-of-the-art.
Entity linking is the task of identifying mentions of entities in text, and linking them to entries in a knowledge base. This task is especially difficult in microblogs, as there is little additional text to provide disambiguating context; rather, authors rely on an implicit common ground of shared knowledge with their readers. In this paper, we attempt to capture some of this implicit context by exploiting the social network structure in microblogs. We build on the theory of homophily, which implies that socially linked individuals share interests, and are therefore likely to mention the same sorts of entities. We implement this idea by encoding authors, mentions, and entities in a continuous vector space, which is constructed so that socially-connected authors have similar vector representations. These vectors are incorporated into a neural structured prediction model, which captures structural constraints that are inherent in the entity linking task. Together, these design decisions yield F1 improvements of 1%-5% on benchmark datasets, as compared to the previous state-of-the-art.