Date post: | 31-Dec-2015 |
Category: |
Documents |
Upload: | marion-morrison |
View: | 245 times |
Download: | 1 times |
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.