机器翻译的层级消岐模式

VIP免费

3.0 牛悦 2024-11-19 4 4 748.06KB 140 页 15积分

侵权投诉

Chapter 1: Introduction

Ambiguity is a very common phenomenon in English. Most English words are

polysemous. For example, the word palm means either a kind of tree or a part of the

hand. Such polysemous words and expressions may trigger lexical ambiguities. The

task to assign an appropriate meaning to these ambiguous words according to given

contexts is called Word Sense Disambiguation (WSD for short). WSD is very vital to

machine translation.

Humans can successfully recognize the appropriate meaning of a word or

expression. For machine, however, it is much more complicated and difficult to make it

out. Furthermore, solutions to lexical ambiguities require many knowledge resources,

such as linguistic information, contextual (pragmatic) information and cultural

information. Due to the limitation of the computer technology, the representation of

most contextual and cultural knowledge remains an impossible issue and the WSD

issue in machine translation still remains unsolved.

With the development of related sciences (such as linguistics and computer

science), a lot of WSD methods are proposed to solve lexical ambiguities.

Researches on several commonly used methods show that none of these methods

is capable of disambiguating all kinds of ambiguities and each has its own

disambiguation reference and application range. For example, WordNet-based method

mainly deals with nouns and takes context nouns as its disambiguation reference. The

method based on co-occurrence features solves polysemous words with co-occurrence

words and the method based on selectional restriction can only be used when a verb or

modifier can strongly restrict the meaning of the noun. Although they can solve these

ambiguities with high accuracy rates in the evaluation, these methods mostly treat

limited kinds of ambiguity and have various shortcomings. If one of them is used to

disambiguate all kinds of polysemous words in a text, the accuracy rate on the whole is

very low. In order to obtain optimal disambiguation efficiency for a text, this thesis

proposes a hierarchical disambiguation model which combines six methods with

different disambiguation references and application ranges.

The hierarchical disambiguation model proposed in this thesis comprises six

steps, i.e., looking up idiom and collocation dictionaries, tagging part-of-speech,

A Hierarchical Disambiguation Model in Machine Translation

disambiguating nouns with context nouns, disambiguating verbs and nouns with

co-occurrence words, solving ambiguous words whose meaning can be strongly

restricted by selectional restrictions and assigning the most frequently used sense to

words without any disambiguation reference.

Evaluation results show that the WSD efficiency of the hierarchical

disambiguation model is much higher than that of any method working alone.

Chapter 2 An Overview of Current Word Sense Disambiguation Methods

Chapter 2 An Overview of Current Word Sense

Disambiguation Methods

Many disambiguation methods have been proposed since the 1950s. Among

them, idiom dictionary and compound words dictionary look-up, part-of-speech

tagging, WordNet-based method, method based on co-occurrence features, method

based on selectional restriction and most frequency method are popular and will be

briefly introduced in the following.

Due to different knowledge sources, each method has its own application range

and disambiguation reference. Application range refers to certain ambiguity

phenomenon to which the method is applicable. Disambiguation reference refers to

surrounding words which are used as semantic reference to determine the appropriate

meaning of the target word. Appropriate methods for a target text can be chosen based

on the type of the text and the application range and disambiguation reference of each

method.

§2.1 Idiom Dictionary and Compound Words Dictionary Look-up

The meaning of a ploysemous word in a fixed collocation can be determined

directly. For example, the word hand in the expression on the one hand means hand#7

(one of two sides of an issue) and the compound word black tea is translated as 红茶

in Chinese. The idiom dictionary and compound words dictionary are necessary to deal

with fixed expressions in natural languages. If meanings of the words in fixed

collocations can be found in dictionaries, other analyses will be avoided.

For some expressions which can be used as either idioms or common phrases,

dictionary look-up may assign wrong meanings to them. Take the phrase black sheep

for example. In the sentence ‘We all thought my youngest brother was the black sheep

in our family (我们都将我的小弟弟视为我们家的败家子)’, the phrase black sheep

acts as an idiom. In the song ‘Baa, baa, black sheep, Have you any wool? Yes, sir, yes,

sir, Three bags full; One for the master, And one for the dame, And one for the little

boy Who lives down the lane (咩咩，黑羊，你有羊毛吗？是的先生，是的先生，满

满三袋：一袋给主人，一袋给夫人，还有一袋给住在巷尾的小男孩)’, black sheep

A Hierarchical Disambiguation Model in Machine Translation

here is just a common noun phrase. The determination of expressions like black sheep

requires other reference information and other WSD methods.

§2.2 Part-of-speech Tagging

Part-of-speech (PoS) tagging means the process that the PoS tagging system tags

the part-of-speech for words in the text. Some words have different meanings

depending on their parts-of-speech, i.e. concurrences which refer to words having two

or more grammar functions, such as part of speech and meanings, in different contexts.

If its part of speech can be determined, its appropriate meaning also can be determined.

In other words, PoS tagging can solve some lexical ambiguities triggered by

concurrences. For example, the word will as a noun means an intention or wish, while

it indicates futurity as an auxiliary verb:

a) He will meet his mother at the airport tomorrow.

b) His will makes him successful.

However, if a word with a certain part of speech is still polysemous, PoS tagging

is not able to disambiguate it. For example, the word plant still has two different

commonly used meanings as a noun: plant life and industrial works. For the sentence

‘He has worked in the plant for 20 years’, if the parser tags the word plant as a noun,

the meaning of the word still cannot be assigned, because of its two different

meanings.

§2.3 WordNet-based Method

WordNet-based method is a kind of knowledge-based methods relying on

machine readable dictionaries which can provide word sense and its sense relation.

Lesk (1986) firstly created a knowledge base to count the overlapping content

words in the sense definition of the ambiguous word and in the context words

occurring nearby and select the sense of the target word whose signature contained the

greatest number of overlaps. Lesk took the word cone in the phrase pine cone for

example to illustrate the method. The appropriate sense of the word cone in the phrase

is chosen from its three senses by comparing the definitions of pine and cone.

pine: 1 kinds of evergreen tree with needle-shaped leaves

2 waste away through sorrow or illness

Chapter 2 An Overview of Current Word Sense Disambiguation Methods

Cone: 1 solid body which narrows to a point

2 something of this shape whether solid or hollow

3 fruit of certain evergreen trees

In this example, sense 1 of pine has two content words overlapping the content

words in sense 3 of cone:evergreen and tree. Hence, if the two words occur in the

same context, the sense of cone can be determined as fruit of certain evergreen trees.

Since Lesk (1986), many researchers used machine-readable dictionaries as

knowledge source for WSD. A disambiguation method using WordNet①is presented by

Agirre (1996) to solve the lexical ambiguity of nouns using noun taxonomy provided by

WordNet.

WordNet developed at the Princeton Cognitive Science Laboratory is a large

freely available lexical database, which takes a hybrid approach to identify, define and

organize word senses. Word senses in WordNet are defined as synsets. In WordNet,

words are represented by their definitions, synonyms, antonyms, hypernyms

(superordinates), hyponyms (subordinates), coordinate terms and meronyms (parts).

WordNet has more than 118,000 word forms and about 90,000 synsets.

WordNet involves various sets of information which can be used to solve the

problem of ambiguity separately or in order depending on the type of text and the

requirement of the WSD model, such as synonyms, hypernym/hyponym, meronyms,

derivationally related terms, domain term and familiarity etc. In this way, the program

can deal with ambiguities based on a large scale of disambiguating information from a

free available database rather than a tagged corpus which is time and labor consuming.

Still take the word plant (noun) for example.

a) Definitions of plant:

Plant 1, works, industrial plant -- (buildings for carrying

on industrial labor; "they built a large plant to manufacture

automobiles")

Plant 2, flora, plant life -- (a living organism lacking the

power of locomotion)

Plant 3-- (something planted secretly for discovery by

another; "the police used a Plant to trick the thieves"; "he

claimed that the evidence against him was a plant")

①WordNet is freely available at http://wordnet.princeton.edu/online/.

A Hierarchical Disambiguation Model in Machine Translation

Plant 4-- (an actor situated in the audience whose acting is

rehearsed but seems spontaneous to the audience)

b) Hypernyms synsets of plant

plant# plant#2 plant#3 plant#4

building complex life form contrivance actor

structure entity scheme

performer

artifact plan of action entertainer

object plan person

entity idea life form

content entity

cognition

psychological feature

c) Synonyms of plant

4 senses of plant

Sense 1

plant, works, industrial plant -- (buildings for carrying on

industrial labor; "they built a large plant to manufacture

automobiles")

=> building complex, complex -- (a whole

structure (as a building) made up of interconnected or related

structures)

Sense 2

plant, flora, plant life -- (a living organism lacking the

power of locomotion)

=> organism, being -- (a living thing that has (or

can develop) the ability to act or function independently)

Sense 3

plant -- (something planted secretly for discovery by

another; "the police used a plant to trick the thieves"; "he

claimed that the evidence against him was a plant")

=> contrivance, stratagem, dodge -- (an

elaborate or deceitful scheme contrived to deceive or evade; "his

testimony was just a contrivance to throw us off the track")

文档加载中……请稍候！
如果长时间未打开，您也可以点击刷新试试。

下载文档到电脑，查找使用更方便

15 积分 4人已下载

立即下载 VIP免费下载

摘要：

Chapter1:Introduction1Chapter1:IntroductionAmbiguityisaverycommonphenomenoninEnglish.MostEnglishwordsarepolysemous.Forexample,thewordpalmmeanseitherakindoftreeorapartofthehand.Suchpolysemouswordsandexpressionsmaytriggerlexicalambiguities.Thetasktoassignanappropriatemeaningtotheseambiguouswordsaccord...

展开>> 收起<<

机器翻译的层级消岐模式.pdf

共140页,预览10页

还剩页未读，继续阅读

机器翻译的层级消岐模式

相关推荐

开通VIP享超值会员特权

作者详情

相关内容

推荐作者

热门标签

举报选择: