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【书名】：Corpus Analysis in Academic Discourse

【作者】：María Luisa Carrió-Pastor (ed.)

【年份】：2020年

【出版社】：Routledge出版社

【简介】：经典议题不同语体的语料库研究最新论文集。

This collection sheds light on the ways in which corpus linguistics and the use of learner corpora might be applied to the study of academic discourse, revealing linguistic and rhetorical patterns and insights into variation across a range of disciplinary genres. Organized into three sections, the book highlights key tools and methodologies in corpus analysis to study such features as discourse markers, lexical bundles, linguistic complexity, lexico-grammatical conventions, and modality in case studies in studies of academic discourse, both in a second language and in English for specific purposes. The volume features examples from disciplinary genres not often covered in the existing literature, including MA theses, academic book reviews, and online student forums. Taken together with the study of learner corpora, the book demonstrates the impact of corpus linguistic tools in better understanding linguistic patterns of specific languages and language use and in turn, their role in helping to identify the needs of language learners. The book will be of interest to students and scholars in corpus linguistics, applied linguistics, and English for Specific Purposes.

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【书名】：Cognitively inspired Natural Language Processing. An Investigation based on Eye-tracking 

【作者】：Abhijit Mishra, Pushpak Bhattacharyya

【年份】：2018年

【出版社】：Springer出版社

【简介】：This book shows ways of augmenting the capabilities of Natural Language Processing (NLP) systems by means of cognitive-mode language processing. The authors employ eye-tracking technology to record and analyze shallow cognitive information in the form of gaze patterns of readers/annotators who perform language processing tasks. The insights gained from such measures are subsequently translated into systems that help us (1) assess the actual cognitive load in text annotation, with resulting increase in human text-annotation efficiency, and (2) extract cognitive features that, when added to traditional features, can improve the accuracy of text classifiers. In sum, the authors’ work successfully demonstrates that cognitive information gleaned from human eye-movement data can benefit modern NLP.

Currently available Natural Language Processing (NLP) systems are weak AI systems: they seek to capture the functionality of human language processing, without worrying about how this processing is realized in human beings’ hardware. In other words, these systems are oblivious to the actual cognitive processes involved in human language processing. This ignorance, however, is NOT bliss! The accuracy figures of all non-toy NLP systems saturate beyond a certain point, making it abundantly clear that “something different should be done.”

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