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Abstract:
Named Entity Disambiguation (NED) is a Natural Language Processing task of linking mentions of named entities is a text to their corresponding entries in a Knowledge Base. It serves as a crucial component in applications such as Semantic Search, Knowledge Base Population, and Opinion Mining. Currently deployed tools for NED are based on sophisticated models that use coherence relation among entities and distributed vectors to represent the entity mentions and their contexts in a document to disambiguate them collectively. Factors that have not been considered yet in this track are the semantics of temporal information about canonical entity
forms and their mentions. Even though temporal expressions in a text give inherent
structural characteristic to it, for instance, it can map a topic being discussed to a certain period of known history, yet such expressions are leveraged no differently
than other dictionary words. In this thesis we propose the first time-aware NED
model, which extends a state-of-the-art learning to rank approach based on joint
word-entity embeddings. For this we introduce the concept of temporal signatures
that is used in our work to represent the importance of each entity in a Knowledge
Base over a historical time-line. Such signatures for the entities and temporal contexts for the entity mentions are represented in our proposed temporal vector space to model the similarities between them. We evaluated our method on CoNLL-AIDA and TAC 2010, which are two widely used datasets in the NED track. However, because such datasets are composed of news articles from a short time-period, they do not provide extensive evaluation for our proposed temoral similarity modeling. Therefore, we curated a dia-chronic dataset, diaNED, with the characteristic of temporally diverse entity mentions in its text collection.
