Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability to alleviate the burden of manual feature engineering. In this paper, we propose a novel neural network model for Chinese word segmentation called Max-Margin Tensor Neural Network (MMTNN). By exploiting tag embeddings and tensorbased transformation, MMTNN has the ability to model complicated interactions between tags and context characters. Furthermore, a new tensor factorization approach is proposed to speed up the model and avoid overfitting. Experiments on the benchmark dataset show that our model achieves better performances than previous neural network models and that our model can achieve a competitive performance with minimal feature engineering. Despite Chinese word segmentation being a specific case, MMTNN can be easily generalized and applied to other sequence labeling tasks.
Most existing graph-based parsing models rely on millions of hand-crafted features, which limits their generalization ability and slows down the parsing speed. In this paper, we propose a general and effective Neural Network model for graph-based dependency parsing. Our model can automatically learn high-order feature combinations using only atomic features by exploiting a novel activation function tanhcube. Moreover, we propose a simple yet effective way to utilize phrase-level information that is expensive to use in conventional graph-based parsers. Experiments on the English Penn Treebank show that parsers based on our model perform better than conventional graph-based parsers.
An event chronicle provides people with an easy and fast access to learn the past. In this paper, we propose the first novel approach to automatically generate a topically relevant event chronicle during a certain period given a reference chronicle during another period. Our approach consists of two core components-a timeaware hierarchical Bayesian model for event detection, and a learning-to-rank model to select the salient events to construct the final chronicle. Experimental results demonstrate our approach is promising to tackle this new problem.
In this paper, we propose a joint model for unsupervised Chinese word segmentation (CWS). Inspired by the "products of experts" idea, our joint model firstly combines two generative models, which are word-based hierarchical Dirichlet process model and character-based hidden Markov model, by simply multiplying their probabilities together. Gibbs sampling is used for model inference. In order to further combine the strength of goodness-based model, we then integrated nVBE into our joint model by using it to initializing the Gibbs sampler. We conduct our experiments on PKU and MSRA datasets provided by the second SIGHAN bakeoff. Test results on these two datasets show that the joint model achieves much better results than all of its component models. Statistical significance tests also show that it is significantly better than stateof-the-art systems, achieving the highest F-scores. Finally, analysis indicates that compared with nVBE and HDP, the joint model has a stronger ability to solve both combinational and overlapping ambiguities in Chinese word segmentation.
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