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.