Chapter 10 Language and Computer

1. Chapter 10 Language and Computer English Linguistics: An Introduction

2. Chapter 10 Language and Computer 0. Warm-up Questions 1. Computational Linguistics 2. CALL 3. Machine Translation 4. Corpus Linguistics

3. 0. Warm-up Questions • In what ways can computer facilitate our language learning? • To what extent do you rely on computer in your English learning? • How to improve the output quality of machine translation? • What is the impact of the Internet on machine translation?

4. 1. Computational Linguistics 1.1 Definition (p226) • A branch of applied linguistics, dealing with computer processing of human language. 1.2 Related subjects • Programmed instruction 编序教学法、程式化教学 • Speech synthesis 言语合成 • Automatic recognition of human speech • Automatic translation of natural languages • Communication between people and computers • Text processing, etc

5. 2. CALL 2.1 CAI, CAL, CALL (p226) • CAI: Computer-assisted Instruction • CAL: Computer-assisted Learning • CALL: Computer-assisted Language Learning 2.2 Phases of CALL • Behavioristic CALL: computer as tutor • Communicative CALL: computer as stimulus • Integrative CALL: multimedia and the Internet

6. 2. CALL 2.3 Types of CALL programs • Davies & Higgins (1985): Gapmaster, Mazes, etc. • Jones & Fortescue (1987): Matchmaster, Wordstore, etc. • Higgins (1993): Customizing, Computer networks, etc 2.4 Advantages and Problems • Advantages • Motivation, adaptive, authenticity, critical thinking • Problems (Limitations of the technology) • ability (human-like interaction), availability (cost), etc.

7. 3. Machine Translation 3.1 Introduction • Definition: the use of machine (usually computers) to translate text (or speech) from one natural L to another. • Types: Unassisted MT and Assisted MT; T2T MT, S2S MT, S2T MT, T2S MT 3.2 History of development • 1950s: independent work by MT researchers • 1960s: hope for good quality • Since 1970s: computer-based tools

8. 3. Machine Translation 3.3 Research methods • Rule-based: Transfer- & dictionary-based, interlingual • Knowledge-based: semantic, pragmatic, real-world • Corpus-based: statistical, example-based 3.4 Advantages and Problems • Advantages: cost-effective, time-saving • Problems: output quality hard to ensure (reasons?)

9. 4. Corpus Linguistics 4.1 Definition (p238) • Corpus: a collection of linguistic data, either compiled as written texts or as transcription of recorded speech. • Corpus linguistics deals with the principles and practice of using corpora in language study. 4.2 Features of the corpus • Representativeness • Finite size • Machine-readable form • A standard reference

10. 4. Corpus Linguistics 4.3 Types of the corpus (p273) In terms of function, there are four common types of corpora: • General corpora: broadly homogeneous • Specialized corpora: for specific purposes • Sample corpora: genre-based • Monitor corpora: gigantic, ever moving store

11. 4. Corpus Linguistics 4.4 For language learning The corpus can be used to • Search for a particular word, sequence of words or even a part of speech in a text; • Retrieve all examples of a particular word; • Compare the different usages of the same word; • Analyse keywords; • Analyse word frequencies; • Find and analyse phrases and idioms; • Create indexes and word lists, etc.

12. 4. Corpus Linguistics 4.4 For language study • Lexical studies: complete and precise definitions and usage of words and phrases. • Grammar: The potential for the representative quantification of a whole language variety. Their role as empirical data for the testing of hypotheses derived from grammatical theory. • Semantics: an empirical objective indicator of a particular semantic distinction, establishing more firmly the notions of fuzzy categories and gradience, etc.