Abstract

In developing a machine translation system, one of the difficult tasks is how to build a transfer dictionary. It has been built by human labor from scratch in most cases. This approach, however, is very ineffective from the viewpoint of cost and time. To avoid this problem, we generate a Korean to Japanese dictionary as a sample, taking advantage of existing linguistic resources, which consist of a Japanese to English dictionary and a Korean to English dictionary for the present goal. First, we extract some sets of English words corresponding to Korean words from a Korean to English dictionary. Second, we search for Japanese words having English equivalents that are similar to Korean counterparts in meaning. Finally, we link the Korean words to Japanese ones. The degree of similarity is determined according to how many translated words are shared between Korean and Japanese. We test 1,000 Korean words extracted at random and get 365 appropriate Japanese words. The result shows that 72% are accurate for a degree of similarity of 0.8 and above.

keywords:

bilingual dictionary, dictionary generation, intermediate language, similarity of translation

INDEX

In the development of a machine translation system, it is necessary to create a bilingual dictionary of the source language and the target language according to the linguistic pair being dealt with, but for such a task, the costs are enormous from the viewpoints of labor and time. In particular, when one of the languages is not that familiar, in other words, when the number of users is comparatively small, it is difficult to secure development personnel familiar with both languages. Sometimes there may exist no bilingual dictionaries usable for humans as reference material.

However, even if bilingual dictionaries do not directly exist for a source language and a target language, the possibility is high that bilingual dictionaries of both the source and target exist in an identical third language, particularly English. In other words, it is conceivable that the generation of a bilingual dictionary through English can be effective¹. By effectively using such linguistic resources, the establishment of methods of generating bilingual dictionaries between various languages can be expected.

A method of generating a bilingual dictionary of a source language and a target language through a third language was proposed by Tanaka et al. [2,3]. However, this effort was confined to the effect of being "useful for revising and supplementing for the vocabulary of existing dictionaries." Although the basic elemental technologies in the method of Tanaka et al. were comprehensively proposed, we believe that there are problems in the usage of these technologies, and we instead attempt the reconstruction of the method towards a method involving engineering usability.

Below, we assume English as our third language, and we investigate a method of generating a bilingual dictionary of a source language and a target language by using a bilingual dictionary of the source language to English and that of the target language to English. In addition, we do not use lexical information of the source and/or target languages in order to verify the practical nature of the method. Based on the fact that the verification of the validity of translation pairs is not easy, we aim at achieving a method of verifying the accuracy of the output translation pair rather than the recall of the translation pair.

A method of generating a bilingual dictionary of a source language and a target language through an "intermediate" language was proposed by Tanaka et al. [2,3]. The outline of their method is as follows (an attempt is made to generate a Japanese-French dictionary with English assumed to be the "intermediate" language).

1.

Create a Japanese-English "harmonized dictionary" that integrates a Japanese-English dictionary and an English-Japanese dictionary, and an English-French "harmonized dictionary" that integrates an English-French dictionary and a French-English dictionary.

2.

By using the "harmonized dictionaries," place English translation sets corresponding to Japanese words and English translation sets corresponding to French words in a "selection area," and judge results having a lot of matching translated words as being in a bilingual relationship ("one time inverse consultation").

3.

By using the "harmonized dictionaries," carry out a second-stage dictionary selection of "JapaneseEnglishFrench" or "French EnglishJapanese", place the last translated sets of French words or Japanese words in a "selection area," and judge the results having a lot of common morphemes as being in a bilingual relationship ("two times inverse consultation").

They report that "Comparing the resulting dictionary with published dictionaries showed that data obtained are useful for revising and supplementing the vocaburary of existing dictionaries," as a result of the above procedure.

We considered that the following problems would appear in the method of Tanaka et al. considering the automatic generation of a bilingual dictionary of Japanese and Korean.

(a)

"Harmonized dictionary" problem.
In order to increase the coverage, an appropriate solution might seenn to be the use of "harmonized dictionaries" that combine dictionaries differing in terms of directivity.² However, because the linguistic nature of Japanese and that of English largely differ, expository translations would appear in large numbers and would be hard to use with the method of Tanaka et al., even if an EJ dictionary were inverted and made into a JE dictionary. The same problem would appear with Korean and English. In other words, "harmonized dictionaries" are perhaps effective in improving the extraction of translation pairs, but there is doubt in their effectiveness towards the accuracy of extracted results.

(b)

Multiple word translation problem.
If the English translation of a word of a source language came to be multiple words, a bilingual dictionary from English to the target language would generally be powerless. In other words, effective translation is best achieved only if the English translation is one word. We also believe that this problem cannot be clarified easily with the correspondences between English and French.

(c)

"Two times inverse consultation" problem.
The description of a bilingual dictionary would perhaps be effective for the improvement of the recall, but we think that it would further reduce the accuracy with increasing fineness. In addition, not much labor-savings could be expected by automatic generation since the need would arise for people to investigate all outputs.

(d)

Source language or target language vocabulary information use problem.
As long as a target language does not involve the contributions of words of a number of users, we believe it is not easy to investigate the correspondences of the source language and target language. This conceivably can largely obstruct the practical use of the method.

(e)

Linguistic characteristics problem.
French and English have closer linguistic characteristics compared with Japanese and English. Considering the generation of a bilingual dictionary of Japanese and Korean, the source language and target language would be in a close relationship and the "intermediate language" would be in a distant relationship. In contrast, if we were to focus on a bilingual dictionary of Chinese and Japanese, the source language, intermediate language, and target language would all be in a distant relationship. Whether it would be proper to handle these at the same level has not been verified.

Although the method of Tanaka et al. can be considered to basically emphasize the generation of translations and aims at the realization of a practical method, it is difficult to say that it functions effectively in the report. Accordingly, we have decided to aim at the establishment of a method involving engineering usability, by reconsidering the method of Tanaka et al.

We decided to set the following presuppositions for our investigation on an alternative method.

(1)

Both a bilingual dictionary from the source language to English and a bilingual dictionary from the target language to English exist.

(2)

Either the source language or the target language cannot be understood (text processing is possible).³

(3)

It is possible to use various lexical information of English.

(4)

It is acceptable to use various lexical information of the target language or source language (not (2) above).

(1) and (2) are necessary conditions. (3) is an optional condition, but it does not rely on the characteristics of the source language and target language. Moreover, the generality of the method is never lost even if this condition is added, since English functions as an actual intermediate language in communications among humans.

In contrast, (4) is realistically possible, but it is necessary to discuss this by separating (1), (2), and (3), since a problem arises in the practical use of the method.

In this paper, however, (3) and (4) hold in the proposal.

On the above assumptions, we attempt to test the following method by concentrating on the generation of a Korean-Japanese dictionary.

First, we assume that harmonized dictionaries are not employed for the reasons in the previous subsection. The linguistic characteristics in Japanese and English largely differ, and so it can be considered that the editing policies of Japanese-English dictionaries and English-Japanese dictionaries (for Japanese use) largely differ. The same can also be said for Korean and English. Accordingly, as a first step, we use only a Korean-English dictionary and a Japanese-English dictionary, with the aim of providing natural Japanese for natural Korean. We use the "one time inverse consultation method" of Tanaka et al. as a method to judge the word correspondences of Korean and Japanese. In other words, we extract English translation word sets corresponding to Korean words from a Korean-English dictionary, and moreover, extract English translation word sets corresponding to Japanese words from a Japanese-English dictionary. Then, we judge those pairs having more common words (from among both English word sets) to be in a bilingual relationship.

We used an online dictionary [5] that "Yahoo! Korea" provides, as our Korean-English dictionary. The scale of this dictionary is 100,000 words. In addition, we used the "The New Anchor Japanese-English Dictionary" [6] of "Gakken" as our Japanese-English dictionary. The scale of this dictionary is 21,170 key words.

To simplify the evaluation, we randomly extracted 1,000 Korean words from a Korean-Japanese dictionary [7] and assumed them to be the words for the evaluation. We searched for a Korean-English dictionary assuming these 1,000 words and obtained English translation word sets by simply extracting English translations included in the search results. The semantic classification was specified with the employed Korean-English dictionary, but a large classification was taken into consideration in the test this time. In addition, for the Japanese-English dictionary, we simply extracted an English translation word set for each key word without considering the semantic classification. We extracted words with a high similarity from these English translation word sets, and we extracted Korean words and Japanese words (giving the English translation word sets) as translation pairs. The following equation was used to define the degree of similarity, (Here, the matching of the English translations was simply tested by the complete matching of character series.)

	Num. of common E translations in A and B 2
	Num. of E translations in A + Num. of E translations in B

where
   A: E translation word set corresponding to a K word according to a KE dictionary,
   B: E translation word set corresponding to a J word according to a JE dictionary.

The judgment of correct or incorrect dealt with pairs with a degree of similarity of 0.5 or more. We got 925 Korean and Japanese word pairs including 409 correct ones. Tables 1 and 2 show the accuracy of pairs with a degree of similarity of 0.5 or more. In Tables 1 and 2, "Mixed" means results including "OK" and "NG (no good)". Some of the "NG" cases were mismatched only in their parts-of-speech, for instance (Example 2) in the following chapter. If parts-of-speech mismatches were to be accepted, the precision would have gone to about 10%. We handled "mixed" as similar to "NG," from the viewpoint that our focus was on the precision of our method, not on the recall.

Degree of Similarity	Number of Pairs					Precision limited to two or more word matches
	Total	OK	(Precision)	Mixed	NG
1.0	89	66	(74.1%)	11	12	82.6%	(19/ 23)
0.9	---	---	---	---	---	---	---
0.8	20	13	(65.0%)	2	5	---	---
0.7	1	0	( 0.0%)	0	1	0.0%	( 0/ 1)
0.6	118	64	(54.2%)	15	39	70.3%	(19/ 27)
0.5	137	64	(46.8%)	28	45	57.1%	(28/ 49)
Total	365	207	(56.7%)	56	102	66.0%	(66/100)

There were cases where mistakes were made on correct or incorrect judgments by merely viewing the degree of similarity. First, we decided to examine the degree of similarity after giving priority to pairs of a large number of matching English translations. From this, some order changes occurred like in the following example (Example 0). In the following example, the number of matching English translations is shown by "mat", the degree of similarity is shown by "sim" and an evaluation by a translator is shown by "ev" (: matched, : meaning is matched but illegal part-of-speech, and : mismatched).

(Korean or Japanese words)					(English translation words)
	mat	sim	ev	K: maseutheo ()	masterproprietor
J1:	2	0.57		masutâ ()	managerownerproprietormaster
J2:	1	0.67		mi-ni tsukeru ()	master
J3:	1	0.67		ichigê ()	master
J4:	1	0.50		danna ()	masterhubby
J5:	1	0.50		oyakata ()	masterboss
J6:	1	0.50		jukutatsu ()	masterbecome proficient in

Next, handling comes to be a problem when the degree of similarity is the same across pairs. Tanaka et al. carried out the exclusion of polysemy by making a graph of the correspondence relations of words among three languages [3]. They analyzed the relationship between correspondence relations and accuracy, while referring to the above classification.

As a result, we decided on the following five classifications: depending on the condition of the matching of English translation word sets, the existence/non-existence of English translations not employed for the correspondences of Korean and Japanese, and whether the obtained Japanese word was one word or multiple words. Table 3 shows the number of conditions corresponding to each of the five classifications.

Type	Classification	Number of Pairs					Precision limited to two or more matchings
		Total	OK	(Precision)	Mixed	NG
(a)		89	66	(74.1%)	11	12	82.6%	(19/ 23)
(b)		199	127	(63.8%)	16	56	63.4%	(64/101)
(c)		53	12	(22.6%)	18	23
(d)		24	2	( 8.3%)	11	11
(e)		635	0	( 0.0%)	0	635

(a) A case of complete matching in the English translation word sets of KE & JE. (Examples 1 & 2)

The precision of the extracted translation pairs is high regardless of whether the obtained Japanese word candidate is one or more. When there is only one English translation word set, the possibility of being able to eliminate errors is high, considering the abundance of polysemy of English words.⁴

	mat	sim	ev	K: teurama ()	dramaplay
J1:	2	1.00		shibai ()	playdrama
J2:	2	1.00		geki ()	dramaplay
J3:	2	1.00		gikyoku ()	dramaplay
J4:	2	1.00		engeki ()	dramaplay
J5:	2	1.00		dorama ()	dramaplay
J6:	1	0.67		asobaseru ()	play
J7:	1	0.67		hiku ()	play
J8:	1	0.67		ensô-suru ()	play
J9:	1	0.67		hane-o nobasu ()	play
Ja:	1	0.67		gokko ()	play
Jb:	1	0.50		yûgi ()	playgame
Jc:	1	0.50		enjiru ()	playperform

	mat	sim	ev	K: piyak ()	jumpleap
J1:	2	1.00		jampu-suru ()	jumpleap
J2:	2	0.80		tobu ()	jumpleaphop
J3:	2	0.67		hiyaku ()	rapidgreatjumpleap
J4:	1	0.67		chôyaku-suru ()	jump
J5:	1	0.50		tobikomu ()	jump intojump
J6:	1	0.50		tobikakaru ()	leap atleap
J7:	1	0.50		chôyaku ()	jumpingjump
J8:	1	0.50		jampu ()	jumpski jump

(b) A case of the English translation word corresponding to one word or more and the obtained Japanese word being limited to one word in principle.⁵(Examples 3 & 4)

The accuracy of the extracted translation pairs is quite high when two or more English translations agree, but it is suspect when there is only one. From the threshold of the degree of similarity (e.g., assuming a threshold of 0.8 or more), it is possible to raise the accuracy. Then again, creating English translation word sets by performing classification for each accepted word and considering the stated order of the English translations (according to the descriptions of KE and JE dictionaries) may be effective in improving the accuracy.

	mat	sim	ev	K: kwanjeom ()	point of viewviewpointstandpointangle
J1:	3	0.86		mikata ()	point of viewviewpointangle
J2:	3	0.86		kanten ()	point of viewviewpointangle
J3:	3	0.67		kakudo ()	anglepoint of view
J4:	2	0.67		shiten ()	point of viewviewpoint
J5:	2	0.67		kenchi ()	standpointpoint of view
J6:	2	0.50		tachiba ()	positionstandstandpointpoint of view

	mat	sim	ev	K: kkoburida()	stoopblowbendcrookcurveinflect
J1:	3	0.60		kâbu ()	curvebendcurvecurveball
J2:	2	0.50		kagameru ()	bendstoop

(c) A case of multiple Japanese words pointing to one matching English translation. (Examples 5 & 6)

If there are non-corresponding English translations between both KE and JE, there is the possibility that the accuracy of translation pairs may be improved by considering synonymous relationships in KE and JE. There are a number of cases where multiple selected Japanese words are in a synonymous relationship, when there is only one obtained English translation from the KE dictionary. Considering the abundance of polesemy of English words may be effective.

	mat	sim	ev	K: salbuthi ()	ones kith and kinrelativekinsfolk
J1:	1	0.50		miuchi ()	relative
J2:	1	0.50		miyori ()	relative

	mat	sim	ev	K: pabwang ()	tathagatabuddha
J1:	1	0.50		hotoke-no ()	buddha-likebuddha
J2:	1	0.50		hotoke ()	the buddhabuddha

(d) A case of two or more English translations of KE and Japanese words corresponding to each in a one-to-one manner. (Examples 7 & 8)

It might not be possible to judge, etc., the relevance only with the utilized dictionary information.

	mat	sim	ev	K: kambang ()	cellward
J1:	1	0.67		byôtô ()	ward
J2:	1	0.67		saibô ()	cell
J3:	1	0.50		denchi ()	batterycell

	mat	sim	ev	K: peomgwa ()	faultwrongwrongdoing
J1:	1	0.50		ochido ()	fault
J2:	1	0.50		itaranu ()	wrong

(e) A case of English translations unable to be found that include English given in the KE dictionary. (Examples 9 & 10)

Here, extraction within the range of the utilized dictionaries is difficult. However, because there are examples like Example 9, there is the possibility of being able to improve the recall of translation pairs by finding correspondences of the English translations considering the polysemy of English words, like with (c).

	mat	sim	ev	K: kaeop ()	family occupationones trade
J :	---	---		(No correspondences)
cf. (J:)				kagyô ()	family businessjob

	mat	sim	ev	K: yeom ()	small stony islandrocky islet
J :	---	---		(No correspondences)

By utilizing English as an intermediate language, we reported a method of automatically generating translation pairs of a source language and a target language with a high accuracy. As a case study, we attempted the extraction of translation pairs of Korean and Japanese by using a KE dictionary and a JE dictionary. According to a trial test using 1,000 Korean words randomly extracted from an online KE dictionary offered by "Yahoo! Korea," the method succeeded in connecting 365 words to Japanese words of the "The New Anchor Japanese-English Dictionary" of "Gakken" and an accuracy of 72% was obtained when the degree of similarity was 0.8 or more.

In this paper, we extracted English translations by string processing with a KE dictionary and a JE dictionary, and evaluated the similarity by string agreement. In other words, we used no linguistic information of Korean, English, and Japanese. Consequently, the results in this paper can be considered to be applicable to cases of generating bilingual dictionaries among languages similar to Japanese or Korean through English.

In the future, we plan on improving the recall of translation pairs while maintaining the accuracy of the translation pairs, by the semantic classification of the vocabulary described in such bilingual dictionaries, as well taking linguistic information of English (i.e., intermediary language as explained in section 3), e.g., synonymous relationships in English and polysemy of English words, into consideration.

We are grateful to Mr. Masahiko Kotani, a translator who largely contributed in the evaluation of the test results.

[1]

URL=http://www.yourdictionary.com/.

[2]

Tanaka, K. & K. Umemura (1994). "Construction of a bilingual dictionary intermediated by a third language". COLING-94, pp.293-303.

[3]

Tanaka, K., K. Umemura & H. Iwasaki (1998). "Construction of a bilingual dictionary intermediated by a third language". Trans. of Information Processing Society of Japan, Vol.39, No.6, pp.1915-1924 (in Japanese).

[4]

Hartmann, R. (1983). "Lexicography: principles and practice". Academic Press.

[5]

URL=http://kr.engdic.yahoo.com/.

[6]

Yamagishi, K & T. Gunji (1991). "The New Anchor Japanese-English Dictionary". Gakken.

[7]

Shogakukan & Kumsung Publishing (1993). "Korean-Japanese Dictionary". Shogakukan.