By Jesús Romero-Trillo
The Yearbook of Corpus Linguistics and Pragmatics addresses the interface among the 2 disciplines and provides a platform to students who mix either methodologies to offer rigorous and interdisciplinary findings approximately language in genuine use. Corpus linguistics and Pragmatics have characteristically represented paths of medical suggestion, parallel yet usually jointly unique and with the exception of. Corpus Linguistics can supply a meticulous method in accordance with arithmetic and information, whereas Pragmatics is characterised by way of its attempt within the interpretation of meant that means in genuine language. This sequence will provide readers perception into how pragmatics can be utilized to provide an explanation for actual corpus facts and likewise, how corpora can illustrate pragmatic intuitions.
The current quantity, Yearbook of Corpus Linguistics and Pragmatics 2014: New Empirical and Theoretical Paradigms in Corpus Pragmatics, proposes leading edge examine types within the liaison among pragmatics and corpus linguistics to give an explanation for language in present cultural and social contexts.
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Extra info for Yearbook of Corpus Linguistics and Pragmatics 2014: New Empirical and Theoretical Paradigms
As inferential processes are costly and depend on several factors, they are predicted to produce lower values of the inter-annotators agreement coefficient. Based on our claim (Grisot et al. 2012) that the configuration of Reichenbachian coordinates should be split into three pairs of two coordinates (E/R, R/S and the inferred E/S) instead of the classical view of three coordinates as Reichenbach proposed. We defined the conceptual content of the Simple Past, as in example (12) to be the pair E < S which bears the focus (from the line E ¼ R, R < S and E < S), in other words ‘situation that happened in the past’ and the conceptual meaning of Present Perfect, as en example (13) to be the pair R ¼ S (from the line E < R, R ¼ S, E < S),11 in other words the “current resulting state of a past situation”.
Human-annotated texts described in this chapter served as training data for machine-learning tools,14 specifically a maximum entropy classifier (Manning and Klein 2003). A classifier is a machine-learning tool that will take data items and place them into one of the available classes (in the present case, narrative and non-narrative) according to a statistical algorithm. The underlying principle of maximum entropy is that, when assigning a class, it should be done uniformly (uniform distributions) unless there is some external knowledge that would instruct the system to do it differently.
Negation is inversely correlated with the use of may so the fact that the learner used may 10 % less often than the native speaker says nothing about proficiency regarding can/may or over-/underuse as it is traditionally used – that 10 % difference is completely due to the learners’ use of negations and, crucially, had the native speaker chosen negations as well, he would have exhibited the same perceived dispreference of may. 2 First Improvements Given the above severe shortcomings of the state-of-the-art over-/underuse counts, what can been done to address this?