Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge. In this paper, we propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge, where a knowledge attention mechanism is designed to ensure the model leveraging the appropriate source of external knowledge based on different context. Experimental results demonstrate the validity and effectiveness of our model, where it outperforms state-of-the-art models by a large margin.
In previous work, external knowledge including manually defined rules @cite_25 @cite_21 , such as number gender requirement of different pronouns, and world knowledge @cite_9 , such as selectional preference @cite_1 @cite_26 and eventuality knowledge @cite_29 , have been proved to be helpful for pronoun coreference resolution. Recently, with the development of deep learning, proposed an end-to-end model that learns contextual information with an LSTM module and proved that such knowledge is helpful for coreference resolution when the context is properly encoded. The aforementioned two types of knowledge have their own advantages: the contextual information covers diverse text expressions that are difficult to be predefined while the external knowledge is usually more precisely constructed and able to provide extra information beyond the training data. Different from previous work, we explore the possibility of joining the two types of knowledge for pronoun coreference resolution rather than use only one of them. To the best of our knowledge, this is the first attempt that uses deep learning model to incorporate contextual information and external knowledge for pronoun coreference resolution.
@cite_26 @cite_9 @cite_21 @cite_1 @cite_29 @cite_25
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This paper presents a new distributed representation based approach to capture commonsense knowledge. Currently, we focus on meanings of verbs and related dependency relations. Built upon such dependency relations, a set of vector space models are developed to capture the meaning of the verb and transfer it to related ambiguous pronouns. To demonstrate the capability of this data-driven approach, we do not incorporate any human knowledge. Among all the vector models without human knowledge, we can achieve the best performance on all of the verb similarity datasets. We can also achieve superior performance on a subset of Winograd Schema challenge dataset, compared with other existing pronoun co-reference and embedding models. While world knowledge has been shown to improve learning-based coreference resolvers, the improvements were typically obtained by incorporating world knowledge into a fairly weak baseline resolver. Hence, it is not clear whether these benefits can carry over to a stronger baseline. Moreover, since there has been no attempt to apply different sources of world knowledge in combination to coreference resolution, it is not clear whether they offer complementary benefits to a resolver. We systematically compare commonly-used and under-investigated sources of world knowledge for coreference resolution by applying them to two learning-based coreference models and evaluating them on documents annotated with two different annotation schemes. Abstract The paper describes the way in which a Preference Semantics system for natural language analysis and generation tackles a difficult class of anaphoric inference problems: those requiring either analytic (conceptual) knowledge of a complex sort, or requiring weak inductive knowledge of the course of events in the real world. The method employed converts all available knowledge to a canonical template form and endeavors to create chains of non-reductive inferences from the unknowns to the possible referents. Its method for this is consistent with the overall principle of “semantic preference” used to set up the original meaning representation. Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both human and extrinsic evaluations demonstrate the quality and effectiveness of ASER.