Title: Extracting, Inferring and Applying Knowledge about Entities
Advisors: Luke Zettlemoyer and Yejin Choi
Supervisory Committee: Luke Zettlemoyer (co-Chair), Yejin Choi (co-Chair), Emily M. Bender (GSR, Linguistics), and Dan Weld
Abstract: Real world entities such as people, organizations and countries play a critical role in text. Language contains rich explicit and implicit information about these entities, such as the categories they belong to, relationships to other entities, and events they participate in. To extract such knowledge, models must recognize entities from text and build representation for entities. However, this is challenging: even in a single document, different expressions (i.e., Google, the search giant, the company, it) point to the same entity and often knowledge can only be inferred from context without being explicitly stated.
In this work, we focus on entities to interpret text. Specifically, we introduce two approaches for extracting and inferring knowledge about entities. The first work focuses on entity-entity sentiment relationship, i.e., who feels positively (or negatively) towards whom, a task which requires reasoning about social dynamics of entities as well as targeted sentiment expressions. The second work infers free-form phrases that describe appropriate types for the target entity. For example, consider the sentences ``Bill robbed John. He was arrested." The noun phrases ``John" and ``he" have specific types, such as ``criminal", that can be inferred from context. For this prediction task, we introduce two novel sources of distant supervision: head words of noun phrases found with an automated parser, and types heuristically extracted from Wikipedia pages of linked named entities.
As a future direction, we propose to apply knowledge about entities to improve core NLP tasks such as coreference resolution and question answering. Lastly, we also propose to create summarizations focused on specific entities and examine how entity types affect the way media describes events they participate in.