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Written by a leading scholar who has been closely involved in language planning in China over many decades, this collection of essays is a critical reflection of the work the Chinese government and academics have undertaken in establishing appropriate policies regarding language standard, language use and language education. The essays contain unique insights into the thinking behind much of the language planning work in China today

Written by a leading scholar who has been closely involved in language planning in China over many decades, this collection of essays is a critical reflection of the work the Chinese government and academics have undertaken in establishing appropriate policies regarding language standard, language use and language education. The essays contain unique insights into the thinking behind much of the language planning work in China today. Li Yuming, Beijing Language and Culture University, China.

China, with the world's largest population, numerous ethnic groups, and vast geographical space, is also rich in languages. Since 2006, China's State Language Commission has been publishing annual reports on 'language life' in China. These reports cover language policy and planning, new trends in language use, and major events concerning languages in mainland China, Hong Kong, Macau, and Taiwan. Now these reports are available in English. Li Wei, Birkbeck College, University of London, London, UK.

AbstractIn this paper I relate the risk premia in the stock and bond markets to the conditional volatility of returns and time‐varying reward‐to‐volatility variables. I find that the relation between the expected returns on the stocks and bonds and the volatility of returns is time varying. I provide an approach for evaluating the relative importance of the time‐varying volatility of returns and reward‐to‐volatility variables to explain the predictability of risk premia for stock and bond returns. I show that changing reward‐to‐volatility variables explain more predictable variation in the risk premia for stocks and bonds than changing volatility of returns.

AbstractIn this paper I conduct tests of an intertemporal asset pricing model using variables that forecast stock returns as the risk factors. I document that the forecasting variables are priced so that expected excess returns are related to their conditional covariances with the forecasting variables. The variability in the covariance risk fails to explain the cross‐sectional and time‐series variation in expected stock returns. Evidence rejects restrictions on the prices of covariance risk imposed by the model with constant volatilities. I also find that an extended model that allows time‐varying conditional volatilities is misspecified.

China, with the world's largest population, numerous ethnic groups, and vast geographical space, is also rich in languages. Since 2006, China's State Language Commission has been publishing annual reports on 'language life' in China. These reports cover language policy and planning, new trends in language use, and major events concerning languages in mainland China, Hong Kong, Macau, and Taiwan. Now these reports are available in English. Li Yuming,Ministry of Education, China; Li Wei, UCL Institute of Education, University College London, UK.

Main description: China, with the world's largest population, numerous ethnic groups, and vast geographical space, is also rich in languages. Since 2006, China's State Language Commission has been publishing annual reports on "language life" in China. These reports cover language policy and planning, new trends in language use, and major events concerning languages in mainland China, Hong Kong, Macau, and Taiwan. Now these reports are available in English.

Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize text classification with high-dimensional feature space. For this reason, this study designed a supervised keyword classification method based on TextRank keyword automatic extraction technology and optimize the model with the genetic algorithm to contribute to modeling the keywords of the topic for text classification.