Internationally recognised as one of the leading texts in its field, this volume offers a comprehensive introduction to vocabulary for language teachers who would like to know more about the way vocabulary works. Two leading specialists make research and theory accessible, providing the background knowledge necessary for practitioners to make informed choices about vocabulary teaching and testing. This second edition retains the popular format of the first edition, and has been rewritten to take account of the many developments in the past 20 years. There is a greater focus on the vocabulary learning process, with new chapters on incidental learning, and intentional learning, and a new wide-ranging discussion of formulaic language. The book now also includes extensive treatment of word lists and vocabulary tests, with explanations of their various strengths and limitations. Updated further reading sections, and new Exercises for Expansion make this volume more invaluable than ever.

/pdf/vocabulary-in-language-teaching-fy20411o7w.pdf

The technique of semantics.

Vocabulary in Language Teaching

Language in relation to a unified theory of the structure of human behavior, 2nd rev. ed.

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Neural Network Methods in Natural Language Processing

This paper reports the extension of the key words method for the comparison of corpora. Using automatic tagging software that assigns part-of-speech and semantic field (domain) tags, a method is described which permits the extraction of key domains by applying the keyness calculation to tag frequency lists. The combination of the key words and key domains methods is shown to allow macroscopic analysis (the study of the characteristics of whole texts or varieties of language) to inform the microscopic level (focussing on the use of a particular linguistic feature) and thereby suggesting those linguistic features which should be investigated further. The resulting 'data-driven' approach presented here combines elements of both the 'corpus-based' and 'corpus-driven' paradigms in corpus linguistics. A web-based tool, Wmatrix, implementing the proposed method is applied in a case study: the comparison of UK 2001 general election manifestos of the Labour and Liberal Democratic parties.

/pdf/from-key-words-to-key-semantic-domains-1wwa0f6519.pdf

From key words to key semantic domains

It is generally accepted that formulaic sequences like take the bull by the horns serve an important function in discourse and are widespread in language. It is also generally believed that these sequences are processed more efficiently because single memorized units, even though they are composed of a sequence of individual words, can be processed more quickly and easily than the same sequences of words which are generated creatively (Pawley and Syder 1983). We investigated the hypothesized processing advantage for formulaic sequences by comparing reading times for formulaic sequences versus matched nonformulaic phrases for native and nonnative speakers. It was found that the formulaic sequences were read more quickly than the nonformulaic phrases by both groups of participants. This result supports the assertion that formulaic sequences have a processing advantage over creatively generated language. Interestingly, this processing advantage was in place regardless of whether the formulaic sequences were used idiomatically or literally (e.g. take the bull by the horns = ‘attack a problem’ vs. ‘wrestle an animal’). The fact that the results also held for nonnatives indicates that it is possible for learners to enjoy the same type of processing advantage as natives.

https://sla.sjtu.edu.cn/thesis/formulaic%20sequences--are%20they%20processed%20more%20quickly%20than%20nonformulaic%20language%20by%20native%20and%20nonnative%20speakers.pdf

Formulaic Sequences: Are They Processed More Quickly than Nonformulaic Language by Native and Nonnative Speakers?

https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1467-968X.1935.tb01254.x

The technique of semantics.

Citations

Vocabulary in Language Teaching

Language in relation to a unified theory of the structure of human behavior, 2nd rev. ed.

Neural Network Methods in Natural Language Processing

From key words to key semantic domains

Formulaic Sequences: Are They Processed More Quickly than Nonformulaic Language by Native and Nonnative Speakers?

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