A lifetime of language experience at each click!

Native speakers learn their mother tongue slowly, from birth, by the constant repetition of common words and phrases in a variety of contexts and situations, within the language community. As foreign language learners, we face considerable disadvantages when compared to children learning their mother tongue. Foreign language learners start later in life, have less time, have fewer opportunities to experience the language, and learn in the restricted environment of the classroom. Teachers and books give us information about many words and phrases, but it is difficult for us to know what we need to focus on and learn thoroughly, and what is less important. The rules and explanations are often difficult for us to understand. A large language corpus represents roughly the amount and variety of language that a native-speaker experiences in a whole lifetime. Learners can now access language corpora. We can check which words and phrases are important, and quickly discover their common meanings, collocations, and structural patterns. It is easier to remember things that we find out ourselves, rather than things that teachers or books tell us. Each click on the computer keyboard can show us the same information in different ways, so we can understand it more easily. We can also get many more examples from a corpus. Teachers and native-speakers can also use corpora, to confirm and enhance their own knowledge of a language, and prepare exercises to guide their students. Each of us can learn at our own level and at our own speed.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Crazy animals and other activities for teaching young learners

This is a book for primary school teachers of English written by primary school teachers of English. It brings together the experience and expertise of teachers from around the world to provide a range of stimulating and exciting classroom activities for the primary classroom. There are 50 tried and trusted activities which have been refined and improved over the years by teachers working in diverse contexts and environments. Children will enjoy practising their English through these stimulating and motivating activities. Over 1 000 teachers were contacted and asked to send their favourite activities for teaching English to young learners. The most original and creative activities received were selected for this book. This book grew out of an Aston University - British Council research project called ‘Investigating Global Practices in Teaching English to Young Learners’.