Chapter 10 Language and Computer

English Linguistics: An Introduction

Chapter 10 Language and Computer

0. Warm-up Questions

1. Computational Linguistics

2. CALL

3. Machine Translation

4. Corpus Linguistics

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?

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

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

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.

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

3. Machine Translation

3.3 Research methods

Rule-based: Transfer- & dictionary-based, interlingual

Knowledge-based: semantic, pragmatic, real-world

Corpus-based: statistical, example-based3.4 Advantages and Problems Advantages: cost-effective, time-saving Problems: output quality hard to ensure

(reasons?)

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

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

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.

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.