Automated natural language processing

Details Automated natural language processing Automated natural language processing

1998-10-14

2004-07-06

Edouard, Patrick N. (Department: 2654)

Data processing: speech signal processing, linguistics, language

Linguistics

Natural language

C704S002000

Reexamination Certificate

active

06760695

ABSTRACT:

TECHNICAL FIELD
The invention relates to automated natural language processing in order to translate automatically from one natural language into another natural language, preferably Japanese to English.
BACKGROUND INFORMATION
Various schemes for the machine-based translation of natural language have been proposed. Typically, the system used for translation includes a computer which receives input in one language and performs operations on the received input to supply output in another language. This type of translation has been an inexact one, and the resulting output can require significant editing by a skilled operator. The translation operation performed by known systems generally includes a structural conversion operation. The objective of structural conversion is to transform a given parse tree (i.e., a syntactic structure tree) of the source language sentence to the corresponding tree in the target language. Two types of structural conversion have been tried, grammar-rule-based and template-to-template.
In grammar-rule-based structural conversion, the domain of structural conversion is limited to the domain of grammar rules that have been used to obtain the source-language parse tree (i.e., to a set of subnodes that are immediate daughters of a given node). For example, given
VP=VT
01
+NP (a VerbPhrase consists of a SingleObject Transitive Verb and a NounPhrase, in that order)
and
 Japanese: 1+2=>2+1 (Reverse the order of VT
01
and NP),
each source-language parse tree that involves application of the rule is structurally converted in such a way that the order of the verb and the object is reversed because the verb appears to the right of its object in Japanese. This method is very efficient in that it is easy to find out where the specified conversion applies; it applies exactly at the location where the rule has been used to obtain the source-language parse tree. On the other hand, it can be a weak conversion mechanism in that its domain, as specified above, may be extremely limited, and in that natural language may require conversion rules that straddle over nodes that are not siblings.
In template-to-template structural conversion, structural conversion is specified in terms of input/output (I/O) templates or subtrees. If a given input template matches a given structure tree, that portion of the structure tree that is matched by the template is changed as specified by the corresponding output template. This is a very powerful conversion mechanism, but it can be costly in that it can take a long period of time to find out if a given input template matches any portion of a given structure tree.
SUMMARY OF THE INVENTION
The automated natural language translation system according to the invention has many advantages over known machine-based translators. After the system automatically selects the best possible translation of the input textual information and provides the user with an output (preferably a Japanese language translation of English-language input text), the user can then interface with the system to edit the displayed translation or to obtain alternative translations in an automated fashion. An operator of the automated natural language translation system of the invention can be more productive because the system allows the operator to retain just the portion of the translation that he or she deems acceptable while causing the remaining portion to be retranslated automatically. Since this selective retranslation operation is precisely directed at portions that require retranslation, operators are saved the time and tedium of considering potentially large numbers of incorrect, but highly ranked translations. Furthermore, because the system allows for arbitrary granularity in translation adjustments, more of the final structure of the translation will usually have been generated by the system The system thus reduces the potential for human (operator) error and saves time in edits that may involve structural accord, and tense changes. The system efficiently gives operators the full benefit of its extensive and reliable knowledge of grammar and spelling.
The automated natural language translations system's versatile handling of ambiguous sentence boundaries in the source language, and its powerful semantic propagation provide further accuracy and reduced operator editing of translations. Stored statistical information also improves the accuracy of translations by tailoring the preferred translation to the specific user site. The system's idiom handling method is advantageous in that it allows sentences that happen to include the sequence of words making up the idiom, without intending the meaning of the idiom, to be correctly translated. The system is efficient but still has versatile functions such as long distance feature matching. The system's structural balance expert and coordinate structure expert effectively distinguish between intended parses and unintended parses. A capitalization expert effectively obtains correct interpretations of capitalized words in sentences, and a capitalized sequence procedure effectively deals with multiple-word proper names, without completely ignoring common noun interpretations.
In one aspect, the invention is directed to an improvement of the automated natural language translation system, wherein the improvement relates to parsing input textual information in a source natural language (preferably Japanese) by transforming at least some of the kanas in the input textual information into alphabetic letters of a target natural language (preferably English) thereby allowing the presence of a word or phrase boundary to be recognized in the middle of a kana. The input textual information includes kanjis and kanas wherein kanjis are ideograms which each has some semantic content and kanas are syllabic characters which each represents a sound without any inherent meaning. The source natural language is one which uses ideograms and syllabic characters but does not mark word or phrase boundaries, as is the case with Japanese.
In another aspect, the invention is directed to another improvement of the automated natural language translation system, wherein the improvement relates to parsing input textual information in a source natural language (preferably Japanese, Korean, or Chinese) by performing concurrently on the input textual information a morphological analysis and a syntactic analysis. The source natural language is one without identifiers marking word or phrase boundaries, as is the case with Japanese, Korean, and Chinese.
The foregoing and other objects, aspects, features, and advantages of the invention will become more apparent from the following description and from the claims.


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