We introduce PreCo, a large-scale English dataset for coreference resolution.The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering.To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of Englishspeaking preschoolers.Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset.Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance.

Publishing structured data and linking them to Linking Open Data (LOD) is an ongoing effort to create a Web of data. Each newly involved data source may contain duplicated instances (entities) whose descriptions or schemata differ from those of the existing sources in LOD. To tackle this heterogeneity issue, several matching methods have been developed to link equivalent entities together. Many general-purpose matching methods which focus on similarity metrics suffer from very diverse matching results for different data source pairs. On the other hand, the dataset-specific ones leverage heuristic rules or even manual efforts to ensure the quality, which makes it impossible to apply them to other sources or domains. In this paper, we offer a third choice, a general method of automatically discovering dataset-specific matching rules. In particular, we propose a semi-supervised learning algorithm to iteratively refine matching rules and find new matches of high confidence based on these rules. This dramatically relieves the burden on users of defining rules but still gives high-quality matching results. We carry out experiments on real-world large scale data sources in LOD; the results show the effectiveness of our approach in terms of the precision of discovered matches and the number of missing matches found. Furthermore, we discuss several extensions (like similarity embedded rules, class restriction and SPARQL rewriting) to fit various applications with different requirements.