Neural Word Segmentation with Rich Pretraining

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201

Neural Reranking for Named Entity Recognition

We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.Comment: Accepted as regular paper by RANLP 201

Ultra-low-frequency gravitational waves from individual supermassive black hole binaries as standard sirens

Ultra-low-frequency gravitational waves (GWs) generated by individual inspiraling supermassive black hole binaries (SMBHBs) in the centers of galaxies may be detected by pulsar timing arrays (PTAs) in the future. These GW signals encoding absolute cosmic distances can serve as bright and dark sirens, having potential to be developed into a precise cosmological probe. Here we show that an SKA-era PTA consisting of 100 millisecond pulsars may observe about 20 bright sirens and 90 dark sirens during a 10-year observation. The bright sirens, together with the CMB data, have comparable capabilities to current mainstream data for measuring the equation of state of dark energy. The dark sirens could make the measurement precision of the Hubble constant far beyond the standard of precision cosmology. Our results indicate that ultra-low-frequency GWs from individual SMBHBs are of great significance in exploring the nature of dark energy and measuring the Hubble constant.Comment: 42 pages, 10 figure