Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, ENTITYNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. Our model is generative and flexible; it can model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. In addition, it can be used for several different tasks such as language modeling, coreference resolution, and entity prediction. Experimental results with all these tasks demonstrate that our model consistently outperforms strong baselines and prior work.
Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield wellperforming TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack. An open-source implementation of the framework and all models described in this paper is available online. 1 16 Predecessors of this task were the update and temporal summarization tasks (Aslam et al., 2015)
Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.
We present a novel method for coreference resolution error analysis which we apply to perform a recall error analysis of four state-of-the-art English coreference resolution systems. Our analysis highlights differences between the systems and identifies that the majority of recall errors for nouns and names are shared by all systems. We characterize this set of common challenging errors in terms of a broad range of lexical and semantic properties.
We present cort, a modular toolkit for devising, implementing, comparing and analyzing approaches to coreference resolution. The toolkit allows for a unified representation of popular coreference resolution approaches by making explicit the structures they operate on. Several of the implemented approaches achieve state-ofthe-art performance.
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