Machine translation apparatus having means for translating polysemous words using dominated codes

A machine translation apparatus includes: a transfer dictionary for translation word searching for storing therein a semantic code representative of semantic categorization of a source language word, a dominated code which is a semantic code of a word relatable to the source language word, and a translation word of a target language corresponding to the dominated code; a dominated code and semantic code searching unit for executing a logical AND operation applicable to a word at each node of an intermediate structure obtained by parsing an inputted source language sentence, to be performed on the dominated code of the each node and the semantic code of a node related to the each node, which are respectively stored in the transfer dictionary for translation word searching, and determining proper ones of the dominated code and the semantic code, respectively and a translation word searching unit for searching a translation word for each node corresponding to the dominated code obtained by the dominated code and semantic code searching unit, from among target language translation words for the source language word stored in the transfer dictionary for translation word searching. With the aforesaid machine translation apparatus, a proper translation word for a polysemous word can be automatically selected.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The present invention relates to a machine translation apparatus capable of 
automatically selecting a suitable translation word and reducing the 
burden on a user. 
2. Description of the Related Art 
New information is increasing rapidly in the 20th century, in what might be 
called a knowledge explosion age. People are impelled to perpetually 
enrich their knowledge in order not to be left behind the times. New 
knowledge comes from both inside and outside one's nation. Ordinary people 
read their mother language faster than foreign languages, so translations 
are important. It is now the time that manual translation should be 
replaced with mechanical translation, i.e., a machine translation system. 
In a machine translation system, the language which is to be inputted for 
translation is called the source language, whereas the translated and 
outputted language is called a target language. For example, in the 
Japanese-Chinese machine translation system, the source language is 
Japanese and the target language is Chinese. The difference in syntax and 
semantics between the source and target languages is an important factor 
which influences the quality of machine translation. Consider the 
following translation example: 
##STR1## 
According to a conventional method, in order to transfer the syntax and 
semantics of a source language to those of a target language in a complete 
and precise form, the data structure of the source language is simplified 
to have a simple intermediate structure capable of processing a broad 
range of information. An example of such an intermediate structure is a 
dependency structure. With the simplified data structure, a translation 
can be processed with fewer transfer rules and with higher efficiency. 
This method is called the transfer system. FIG. 6 is a flow chart 
illustrating the translation procedure of the transfer system. As shown in 
FIG. 6, a machine translation apparatus using the transfer system is 
constructed of, in addition to a reference dictionary, (1) a source 
language parsing unit, (2) an intermediate structure transfer unit, and 
(3) a target language generating unit. The most difficult and significant 
operation performed by the intermediate structure transfer unit is to 
search for an optimum translation of a source language word. Languages 
have many meanings, and almost each word is polysemous. A translation word 
should be selected therefore in accordance with the meaning of the source 
word. In other words, a suitable translation word cannot be selected 
unless the meaning of the word in the source language can be grasped 
correctly. For example, the Japanese word " " has at least the following 
five meanings and the translation equivalents thereof change 
correspondingly: 
##STR2## 
As will be understood from the above example, it is most important for the 
intermediate structure transfer unit to efficiently grasp the meaning of 
each word of the source language. Conventional techniques dealing this 
subject are classified into the following two types: 
(I) the user participation system, and 
(II) the reference dictionary system, which refers to a dictionary storing 
all possible translation equivalents. 
These two systems will be described below. 
(I) User Participation System 
There is disclosed, for example, in Japanese Patent Laid-open Publication 
No. 61-260367, a technique whereby translation equivalents having the 
highest use frequency of use are selected from a reference dictionary, and 
the user determines improper translation equivalents and corrects them. 
The determination and correction by the user are registered in a learning 
file for reference in the succeeding selection of translation equivalents, 
whereby the use of the reference dictionary to select translation 
equivalents of a higher use frequency makes it possible to ensure improved 
translation. FIG. 5 is a block diagram showing the structure of a machine 
translation apparatus using this method. Consider the following two 
sentences of a source language (in English): 
(1) I write a letter. 
(2) And, I mail the letter. 
It is assumed that translation words for "letter" stored in the 
English-Japanese dictionary are: 
1. 
2. 
Using the translation word (1), the English sentence (1) is translated into 
" ". Then, the user changes the translation word for "letter" to " " by 
using an interactive function of the apparatus to thereby obtain the 
translation " ". The changed result is stored in a learning file of an 
external storage as learnt experience. Using this learnt experience, the 
sentence (2) is translated into " " and never into " ". 
(II) Reference Dictionary System 
For example, a Knowledge-Base English-To-Chinese Machine Translation System 
(KBMTS) developed by Electronics Research and Service Organization of the 
Industrial Technology Research Institute (ERSO, ITRI) in Taiwan, ROC, as 
described in "Electronics Development Monthly" (Issue No. 122, pp. 9 to 
23, February 1988), uses a semantic marker system. As shown in FIG. 7, 
which refers to the illustration on page 20 of the above-mentioned 
literature, all possible translation equivalents are registered in a 
reference dictionary, and in selecting a proper translation equivalent of 
a word, this system picks up semantic markers of related words and 
processes necessary operations relative to the semantic markers by using 
the reference dictionary to obtain the proper translation word. 
For example, in determining the translation word for the verb "take" in the 
phrase "take a bus", since a word related with the verb "take" is the 
object word "bus", the semantic marker for "bus" can be identified as 
[vehicle] upon reference to the noun in the reference dictionary. 
Accordingly, as a result of reference processing, the most suitable 
translation word for "take" is found to be " " in Chinese. Then, the 
correct translation of the phrase becomes " " in Chinese. 
The above-described technique "(I) User Participation System" provides 
translations of good quality. However, there are the following problems. 
Namely, experiences registered in the learning file of the external 
storage are all short-term experiences, and they are of no use for 
translation processing which requires long-term experiences. It is 
necessary also to select a proper learning file in dependence on what is 
to be translated, and the user is required to participate in the 
translation, which is not automatically carried out. This system is 
applicable only to conversational translations of small range. 
The "(II) Reference Dictionary System" can obtain proper translation 
equivalents on condition that the corresponding data have been registered 
in a transfer reference dictionary. In other words, the transfer reference 
dictionary should be provided with a full and complete semantic marker 
system. If it is not full and complete, the same data may be present more 
than one in the dictionary, or memories may be used wastefully, resulting 
in poor efficiency. 
In view of the above problems, the present invention aims at providing an 
automatic translation apparatus of high quality and efficiency capable of 
automatically searching for a proper translation word while reducing the 
burden on the user, and capable of registering data in a transfer 
dictionary in a specific manner so as to store a maximum amount of data. 
SUMMARY OF THE INVENTION 
In order to solve the above objects, the machine translation apparatus of 
this invention comprises: a transfer dictionary for translation word 
searching, said transfer dictionary storing therein a semantic code 
representative of semantic categorization of a source language word, a 
dominated code which is a semantic code of a word which can be related to 
said source language word, and a translation word of a target language 
corresponding to said dominated code; a dominanted code and semantic code 
searching unit for executing a logical AND operation, for a word at each 
node of an intermediate structure obtained by parsing an inputted source 
language sentence, between said dominated code of said each node and said 
semantic code of a node related to said each node, respectively stored in 
said transfer dictionary for translation word searching, and determining 
proper ones of said dominated code and said semantic code; and a 
translation word searching unit for searching a translation word for each 
node corresponding to said dominated code obtained by said dominated code 
and semantic code searching unit, from target language translation words 
for said source language word stored in said transfer dictionary for 
translation word searching. 
With the machine translation apparatus constructed as specified above 
according to this invention, a dominated code of a word is determined by a 
semantic code of a related node, and a translation word is selected in 
accordance with the dominated code. Therefore, the proper translation word 
for a polysemous word can be automatically selected, thereby improving the 
efficiency of machine translation and reducing the burden on the user.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
The present invention adopts a semantic categorization method in place of 
the conventionally used semantic marker method. In the following 
embodiment, the semantic categorization method explained in the synonym 
dictionary published by Japanese Kadokawa Shoten (1985) is used. This 
semantic categorization method uses a hexadecimal four digit number to 
indicate all information of a word. The categorization is divided into a 
large categorization (indicated by the first digit of the number), middle 
categorization (indicated by the second digit), small categorization 
(indicated by the third digit), and fine categorization (indicated by the 
fourth digit). In this synonym dictionary, all words are classified into 
ten large categorizations including "nature", "property", "change", 
"action", "feeling", "person", "inclination", "society", "culture", and 
"article". Each large categorization is classified into ten middle 
categorizations. In this embodiment, the following format with the 
character s added at the left of a four digit number is used. 
s0 (belongs to "nature") 
s02 (belongs to "weather" in "nature") 
s028 (belongs to "wind" in "weather") 
s028a (belongs to "pressure" in "wind") 
With such hierarchic categorization codes, the meaning range of an upper 
order character in the semantic code is broader than a lower order 
semantic code character. That is, the lower the order of a semantic code 
character, the narrower the meaning range thereof becomes. Semantic codes 
for all categorizations do not need to be stored, but only those semantic 
codes necessary for a particular application are registered, thereby 
reducing memory capacity. In addition, since a semantic code is 
represented by using numerals, it becomes possible to effect mathematical 
processing such as a logical AND operation, collation of strings, etc., 
further to process semantic categorization codes, and to obtain 
significant information which is generated by the use of semantic 
categorization codes. 
The translation equivalent for each word changes with the use of a related 
word. For example, the Japanese word " " has different Chinese translation 
equivalents such as " ", " ", " ", " " and " " in correspondence with an 
object thereof. The word " " has therefore at least five semantic codes. 
An object is a word related to a verb, and the object dominates the 
meaning of the verb. Namely, the semantic code of an object word is a 
dominated code of the verb. If a related word to a source language word 
can be discriminated through parsing, a logical AND operation between 
possible semantic codes of a word under processing and dominated codes of 
its related or adjacent word allows a dominated code of the word under 
processing to be identified. The proper translation equivalent and 
semantic code of the word under processing can be obtained by referring to 
a transfer dictionary for translation word searching by using the obtained 
dominated code as a key. If the transfer dictionary for translation word 
searching does not include a translation word corresponding to the 
dominated code of the word under processing, a default or first priority 
semantic is used as the dominated code of the word under processing. The 
format of the transfer dictionary for translation word searching of this 
embodiment is shown in FIGS. 3A-1, 3A-2, 3B-1, and 3B-2. A noun (excepting 
an adjective modifier) dominates the meaning of a verb, adjective, or 
adjective modifier, so the semantic code of a noun becomes a dominated 
code of a verb, adjective, or adjective modifier. The semantic code of a 
verb or adjective is a dominated code of an adverb, and the semantic code 
of an adverb is a dominated code of another adverb. Accordingly, if a noun 
(excepting an adjective modifier) is determined, dominated codes and 
translation equivalents of other words can be obtained by means of an 
logical AND operation and by using the transfer dictionary for translation 
word searching. The semantic code of a noun is determined in the following 
manner. Namely, a logical AND operation is performed between all possible 
semantic codes of a noun under processing and dominated codes of an 
adjacent or related word, and the code with the highest frequency of 
occurrence obtained by the calculation is assumed as the semantic code of 
the word. If the calculation result is null, then this semantic code of a 
noun is a default semantic code. This operation will be further described 
by using the following example of the intermediate structure. 
In this example, the Japanese word at the top of the diagram can be spelled 
"hiku" in western characters. At the bottom, the Japanese word on the left 
can be spelled "watashi" and the Japanese word on the right can be spelled 
"jibiki." 
##STR3## 
Here, the s codes are the semantic code(s) of the word at each node, and 
the d codes are the dominate code(s) of the word at each node. As 
described previously, a logical AND operation is performed between the 
semantic codes s5010 and s848e respectively of the nouns " " ("watashi") 
and " " ("jibiki") and all the dominated codes of the verb " " ("hiku") so 
that the dominated code s848e is identified. Referring to the transfer 
dictionary for translation word searching, the Chinese translation 
equivalents for the words ("watashi"), " ", " " ("jibiki") and " " 
("hiku") can be automatically obtained as " " (meaning "I" in English), " 
" ("dictionary") and " " ("look up"). 
FIG. 1 shows the structure of the arrangement of a machine translation 
apparatus of this invention. Referring to FIG. 1, block 10 represents a 
source language input unit such as a keyboard from which words of a source 
language to be processed are entered. Block 15 represents a source 
language parsing and intermediate form generating unit wherein the syntax 
and semantics of an inputted word are parsed by using a parsing dictionary 
1 to obtain an intermediate structure of the source language, and the 
obtained structure is stored in a buffer 45. A syntax and semantics 
difference adjusting unit 20 fetches the intermediate structure of the 
source language from the buffer 45, and, while referring to a transfer 
dictionary 2 for difference adjusting, changes the intermediate structure 
of the syntax and semantics of the source language to another intermediate 
structure of the target language which is then stored in the buffer 45. A 
dominated code and semantic code searching unit 25 receives the 
intermediate structure of the target language obtained by the syntax and 
semantics difference adjusting unit 20, searches all possible dominated 
and semantic codes of each word of the intermediate structure and of its 
related word, with reference to the transfer dictionary 3 for translation 
word searching, identifies the dominated code and semantic code of the 
word under processing in accordance with the processes shown in FIGS. 2A, 
2B and 2C, and stores the processing results in the buffer 45. A transfer 
or translation word searching unit 30 refers to the transfer dictionary 3 
for translation word searching, to thereby search for the proper 
translation equivalent of the word whose dominated or semantic codes have 
been stored in the buffer 45, and stores the searched translation 
equivalent in the buffer 45. A target language generation unit 35 
transforms the intermediate structure (also called deep structure) of the 
target language in the buffer 45 stored by the transfer word searching 
unit 30 into the surface structure of the target structure to obtain the 
final translation result which is stored in the buffer 45. A target 
language output unit 40 outputs the translation results stored in the 
buffer 45. 
The operation of the machine translation apparatus of this embodiment 
constructed as above will be described using the sample sentence " " ("He 
wore a grave look"). The Japanese words in this sample sentence can be 
spelled "kare wa muzukashi kao wo shita" in western characters. Referring 
to FIG. 1, this sentence is first entered from the source language input 
unit 10 and sent to the source language parsing and intermediate form 
generating unit 15. Referring to the parsing dictionary 1, the source 
language parsing and intermediate form generating unit 15 parses the words 
and develops them into an intermediate structure which is then stored in 
the buffer 45. The syntax and semantics difference adjusting unit 20 
fetches the stored intermediate structure of the source language from the 
buffer 45, and, while referring to the transfer dictionary 2 for 
difference adjusting, changes it to another intermediate structure of the 
object language to thereby obtain the intermediate structure (dependency 
structure) shown in FIG. 4 which is stored in the buffer 45. Next, as 
described previously, the dominated code and semantic code searching unit 
25 determines dominated codes and semantic codes of each word in 
accordance with the processes shown in FIGS. 2A, 2B and 2C. The operation 
of this dominated code and semantic code searching unit 25 will be 
described in detail. 
First, the initial values for the intermediate structure shown in FIG. 4 
are set at an initialization step 50 shown in FIGS. 2A, 2B and 2C. 
Referring to the transfer dictionary 3 for translation word searching 
which stores therein related words as s own in FIGS. 3A-1, 3A-2, 3B-1, and 
3B-2, the following results are obtained at a node detecting step 51 for 
picking up respective nodes i. 
##STR4## 
where D(i) represents all dominated codes at node i, S(i) all semantic 
codes at node i, and R(i) all related nodes i. 
Next, at a noun node judgement step 52 and adjective word judgement step 53 
shown in FIG. 2A, it is judged that there are nodes (, or "kare"), (, or 
"kao") to be processed. Then, the operation proceeds to a step 61. Since 
each of two nodes (, or "kare"), (, or "kao") has a related node, the 
operation proceeds to a logical AND operation step 62 to determine the 
dominated code for the two words (, or "kare"), (, or "kao"). Sets T 
obtained by performing logical AND operations between the dominated codes 
of the node " " ("kao") and the two related nodes " " ("muzukashi") and " 
" ("suru") are as follows: 
##STR5## 
In the similar manner, the result of a logical AND operation between the 
dominated codes of the node " " ("kare") and the related node " " ("suru") 
is given by: 
##STR6## 
The processing for the nodes (, or "kao"), (, or "kare") proceed either to 
the step 66 or 64, depending upon the judgement result at the step 63. In 
this case, the operation proceeds to the step 66 and the following results 
are obtained: 
##STR7## 
where "dominate (i)" represents a dominate code of node i. At the step 65, 
the following results are obtained: 
##STR8## 
where "meaning (i)" represents a semantic code of node i. Thereafter, the 
operation returns to the noun judgement step 52 shown in FIG. 2A. There is 
no noun node to be processed, so that the operation proceeds to a modifier 
node judgement step 54. In this case, there is only a node " " 
("muzukashi"), so that the operation enters the processing shown in FIG. 
2C. First, at a step 80 it is judged as not null for the node R (, or 
"muzukashi"). Then, at a judgement step 81 it is judged as not null, as 
described above, with respect to "meaning (, or "keo")", and thereafter at 
a step 85 the following result obtained: 
dominate (, or "muzukashi")=s320 
And at a step 83 the following result is obtained: 
meaning (, or "muzukashi")=s492a 
At the succeeding step 84, the operation returns to the modifier node 
judgement step 54 shown in FIG. 2A. Since there is no other modifier node, 
the operation proceeds to a verb node judgement step 55. Upon judgement 
that there is a node (, or "suru"), the operation enters again into the 
processings shown in FIG. 2C. In the similar manner as above, there are 
obtained: 
##STR9## 
Then the operation returns to the verb node judgement step 55 shown in 
FIG. 2A. At this time, since there is no node to be processed at the verb 
node judgement step 55, adverb node judgement step 56 and other step 57, 
the selection of a dominated code and semantic code is terminated. 
Thereafter, the operation proceeds to the transfer word searching unit 30 
shown in FIG. 1, where a transfer word or translation word is selected 
from the transfer dictionary 3 for translation word searching, in 
accordance with the obtained dominated code for respective nodes. As seen 
from the contents of the transfer dictionary 3 for translation word 
searching shown in FIGS. 3A-1 to 3B-2, the translation word for " " 
("kao") corresponding to the dominated code s320 is " " (meaning 
"expression" in English), so that this translation word is selected from 
the dictionary. The other translation words are also selected in a similar 
manner, and the following results are obtained: 
##STR10## 
wherein lex (i) represents a translation word at node (i). 
The target language generation unit 35 assembles these translation words to 
generate a target language sentence, in accordance with the intermediate 
structure obtained at the syntax and semantics difference adjusting unit 
20. The obtained Chinese sentence, which means "He wore a grave look" in 
English) is: 
##STR11## 
which is outputted from the target language output unit 40. 
According to this embodiment executing the above operations, an improper 
translation such as " " (in English, "He did a difficult expression") will 
never occur, but a sentence of proper translation can be obtained. 
According to the present invention, the meanings of not only a particular 
node to be processed but also its, related node are considered in 
selecting a proper translation word. Accordingly the problem associated 
with a polysemous word can be solved, thereby improving the quality of 
automatic machine translation and reducing the burden on the user. 
Further, significant information can be obtained through mathematic 
calculation of semantic and dominated codes, so that it is not necessary 
to store all associated information in a dictionary, thereby reducing 
memory capacity while retaining good efficiency and practicability.