"Legal translation trainees are frequently not experts in the field of Law. This poses considerable problems for legal translator trainers when attempting to introduce their trainees into the legal discourse community, requiring them to translate texts which are completely alien to their prior experience and social practices. In this paper we propose a discourse analysis methodology adapted from Fairclough’s model ( 1996) which provides trainees with the tools to develop a structured analytical process when approaching the translation of legal texts. Traditionally translation classes revolve around the text to be translated, and more specifically the terminology which poses problems for the trainees. In this model trainees are guided through a step-by step procedure which firstly situates the text within the social process and social events which surround it. By locating the text within the discursive practice (production, distribution, consumption) the trainees become familiar with the internalized social structures and conventions governing the text, allowing them access to what Fairclough (ibid.) calls “members resources”. When this information is combined with the social practice in which the text participates, seemingly obscure elements in the text become immediately clearer. The process in then also applied in the Target Language and Target Culture to discover whether parallel discursive and social practices exist, thereby leading to parallel or similar texts. Only then will the translation process proper commence.
The fact that knowledge is no longer fixed, but constantly evolving, and the speed at which new knowledge appears online have contributed to our sense of “information overload,” Weinberger said. And that leads to another way that our evolving sense of knowledge is transforming how we learn: We must learn to accept that true mastery is impossible.
"One of the buzzphrases associated with the social web is sentiment analysis. This is the ability to determine a person’s opinion or state of mind by analysing the words they post on Twitter, Facebook or some other medium.
Much has been promised with this method—the ability to measure satisfaction with politicians, movies and products; the ability to better manage customer relations; the ability to create dialogue for emotion-aware games; the ability to measure the flow of emotion in novels; and so on.
The idea is to entirely automate this process—to analyse the firehose of words produced by social websites using advanced data mining techniques to gauge sentiment on a vast scale.
But all this depends on how well we understand the emotion and polarity (whether negative or positive) that people associate with each word or combinations of words.
Today, Saif Mohammad and Peter Turney at the National Research Council Canada in Ottawa unveil a huge database of words and their associated emotions and polarity, which they have assembled quickly and inexpensively using Amazon’s crowdsourcing Mechanical Turk website. They say this crowdsourcing mechanism makes it possible to increase the size and quality of the database quickly and easily."
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