Unsupervised word representations have demonstrated improvements in predictive generalization on various NLP tasks. Most of the existing models are in fact good at capturing the relatedness among words rather than their ''genuine'' similarity because the context representations are often represented by a sum (or an average) of the neighbor's embeddings, which simplifies the computation but ignores an important fact that the meaning of a word is determined by its context, reflecting not only the surrounding words but also the rules used to combine them (i.e. compositionality). On the other hand, much effort has been devoted to learning a single-prototype representation per word, which is problematic because many words are polysemous, and a single-prototype model is incapable of capturing phenomena of homonymy and polysemy. We present a neural network architecture to jointly learn word embeddings and context representations from large data sets. The explicitly produced context representations are further used to learn context-specific and multi-prototype word embeddings. Our embeddings were evaluated on several NLP tasks, and the experimental results demonstrated the proposed model outperformed other competitors and is applicable to intrinsically "character-based" languages.

Ganoderma lucidum (G. lucidum, Lingzhi) is a well-known herbal medicine with a variety of pharmacological effects. Studies have found that G. lucidum has pharmacological effects such as antioxidant, antitumor, anti-aging, anti-liver fibrosis, and immunomodulation. The main active components of G. lucidum include triterpenoids, polysaccharides, sterols, peptides and other bioactive components. Among them, the triterpenoids and polysaccharide components of G. lucidum have a wide range of anti-liver fibrotic effects. Currently, there have been more reviews and studies on the antioxidant, antitumor, and anti-aging properties of G. lucidum. Based on the current trend of increasing number of liver fibrosis patients in the world, we summarized the role of G.lucidum extract in anti-liver fibrosis and the effect of G. lucidum extract on liver fibrosis induced by different pathogenesis, which were discussed and analyzed. Research and development ideas and references are provided for the subsequent application of G. lucidum extracts in anti-liver fibrosis treatment.

Existing neural dependency parsers usually encode each word in a sentence with bi-directional LSTMs, and estimate the score of an arc from the LSTM representations of the head and the modifier, possibly missing relevant context information for the arc being considered. In this study, we propose a neural feature extraction method that learns to extract arc-specific features. We apply a neural network-based attention method to collect evidences for and against each possible head-modifier pair, with which our model computes certainty scores of belief and disbelief, and determines the final arc score by subtracting the score of disbelief from the one of belief. By explicitly introducing two kinds of evidences, the arc candidates can compete against each other based on more relevant information, especially for the cases where they share the same head or modifier. It makes possible to better discriminate two or more competing arcs by presenting their rivals (disbelief evidence). Experiments on various datasets show that our arc-specific feature extraction mechanism significantly improves the performance of bi-directional LSTM-based models by explicitly modeling long-distance dependencies. For both English and Chinese, the proposed model achieve a higher accuracy on dependency parsing task than most existing neural attention-based models.

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