Entity typing is the task of assigning semantic types to the entities that are mentioned in a text.In the case of fine-grained entity typing (FET), a large set of candidate type labels is considered.Since obtaining sufficient amounts of manual annotations is then prohibitively expensive, FET models are typically trained using distant supervision.In this paper, we propose to improve on this process by pre-training an entity encoder such that embeddings of coreferring entities are more similar to each other than to the embeddings of other entities.The main problem with this strategy, which helps to explain why it has not previously been considered, is that predicted coreference links are often too noisy.We show that this problem can be addressed by using a simple trick: we only consider coreference links that are predicted by two different off-the-shelf systems.With this prudent use of coreference links, our pretraining strategy allows us to improve the stateof-the-art in benchmarks on fine-grained entity typing, as well as traditional entity extraction.

Open information extraction (OIE) is the task of extracting facts "(Subject, Relation, Object)" from natural language text.We propose several new methods for training neural OIE models in this paper.First, we propose a novel method for computing syntactically rich text embeddings using the structure of dependency trees.Second, we propose a new discriminative training approach to OIE in which tokens in the generated fact are classified as "real" or "fake", i.e., those tokens that are in both the generated and gold tuples, and those that are only in the generated tuple but not in the gold tuple.We also address the issue of repetitive tokens in generated facts and improve the models' ability to generate implicit facts.Our approach reduces repetitive tokens by a factor of 23%.Finally, we present paraphrased versions of the CaRB, OIE2016, and LSOIE datasets, and show that the models' performance substantially improves when trained on datasets augmented by such data.Our best model beats the SOTA of IMoJIE on the recent CaRB dataset, with an improvement of 39.63% in F 1 score.

The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form subject, predicate, object .For example, given the sentence »Beethoven composed the Ode to Joy.«, we are expected to extract the triple Beethoven, composed, Ode to Joy .In this work, we systematically compare different neural network architectures and training approaches, and improve the performance of the currently best models on the OIE16 benchmark (Stanovsky and Dagan, 2016) by 0.421 F 1 score and 0.420 AUC-PR, respectively, in our experiments (i.e., by more than 200% in both cases).Furthermore, we show that appropriate problem and loss formulations often affect the performance more than the network architecture.Ludwig van Beethoven was a world -famous composer of classical music .