  Much of the existing work on text novelty detection has been studied at the
topic level, i.e., identifying whether the topic of a document or a sentence is
novel or not. Little work has been done at the fine-grained semantic level (or
contextual level). For example, given that we know Elon Musk is the CEO of a
technology company, the sentence "Elon Musk acted in the sitcom The Big Bang
Theory" is novel and surprising because normally a CEO would not be an actor.
Existing topic-based novelty detection methods work poorly on this problem
because they do not perform semantic reasoning involving relations between
named entities in the text and their background knowledge. This paper proposes
an effective model (called PAT-SND) to solve the problem, which can also
characterize the novelty. An annotated dataset is also created. Evaluation
shows that PAT-SND outperforms 10 baselines by large margins.
