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"paper_id": "C80-1045",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T13:05:38.409894Z"
},
"title": "AN AUTOMATIC TRANSLATION SYSTEM OF NON-SEGMENTED KANA SENTENCES INTO KANJI-KANA SENTENCES",
"authors": [
{
"first": "Hiroshi",
"middle": [],
"last": "Makino",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Osaka University Machikaneyama-eho",
"location": {
"postCode": "560",
"settlement": "Toyonaka",
"region": "Osaka",
"country": "JAPAN"
}
},
"email": ""
},
{
"first": "Makoto",
"middle": [],
"last": "Kizawa",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Library",
"location": {
"addrLine": "and Information Science Yatabe-machi, Tsukuba-gun, Ibaraki-ken 305",
"country": "JAPAN Sum~lary"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This paper presents the algorithms to solve the two main problems comprised in the automatic Kana-KanJi translation system, in which the input sentences in Kana are translated into ordinary Japanese sentences in Kanji and Kana : the segmentation of non-segmented sentences into Bunsetsu and the word identification from homonyms. Employing this algorithm, non-segmented Kana input sentences could be automatically translated into KanJi and Kana output sentences with 96.2 per cent success.",
"pdf_parse": {
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"abstract": [
{
"text": "This paper presents the algorithms to solve the two main problems comprised in the automatic Kana-KanJi translation system, in which the input sentences in Kana are translated into ordinary Japanese sentences in Kanji and Kana : the segmentation of non-segmented sentences into Bunsetsu and the word identification from homonyms. Employing this algorithm, non-segmented Kana input sentences could be automatically translated into KanJi and Kana output sentences with 96.2 per cent success.",
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"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "In the computer processing of the Japanese language informations, the input method is much more difficult than in other Indo-European languages because thousands of kinds of characters in mainly two classes, KanJi(ideograms) and Kana(phonograms), are used together in writing regular sentences.",
"cite_spans": [],
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"section": "Introduction",
"sec_num": null
},
{
"text": "Conventional Japanese typewriters are equipped with least 2000 KanJi(Chinese characters) which are frequently used in daily use. A typewrite of this sort is difficult for us to handle and its typing speed is much lower than that of alphabetic typewriters because operators must look for characters one by one.",
"cite_spans": [],
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"section": "Introduction",
"sec_num": null
},
{
"text": "One of the most promising inputmethods to overcome this intrinsic input difficulty is Kana-KanJi translation system, in which all the sentences are input with Kana only using a regular 44-Key keyboard and then translated into regular KanJi-Kana sentences automatically in the computer. The automatic translation system consists of two processes; the segmentation and the word identification processes.",
"cite_spans": [],
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"section": "Introduction",
"sec_num": null
},
{
"text": "The problems in Kana-KanJi translation are: (a) segmentation of input sentences. (b) word identification from homonyms. These problems are basic in the processing of Japanese sentences as language informations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
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"text": "Japanese sentences in KanJi and Kana have no spaces between words as English ones do. However, in order to make the computer process Kana sentences easy, it would be necessary to put a space as a segmental symbol between words or some units in sentences. Therefore, some spacing methods, listed in Fig.l (concluding non-segment-ed sentence for convenience), was already adopt-13 ed in Kana-Kanji translation systems. -(I) genzai jinrui ha sugure ta me to yubisaki no kankaku wo mot te iru.",
"cite_spans": [],
"ref_spans": [
{
"start": 298,
"end": 303,
"text": "Fig.l",
"ref_id": null
}
],
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"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
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"text": "(2) genzai jinrui ha sugure ta me to yubisaki no kankaku wo mot teiru.",
"cite_spans": [],
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"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
},
{
"text": "(3) genzai jinruiha sugureta meto yubisaklno kankakuwo motteiru.",
"cite_spans": [],
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"eq_spans": [],
"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
},
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"text": "(4) genzaiJinrui ha sugu reta me to yubisaki no kankaku wo mot teiru.",
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"section": "The problem 9 iP Kana-Kap~i translation",
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"text": "(5) genzaiJinruihasuguretametoyubisakinokankskuwomotteiru. (i) segmented between words (2) segmented between an independent word and a sequence of dependent words (3) segmented between Bunsetsu (4) segmented between KanJi and Kana (5) non-segmented However, these pre-editing methods of word segmentation or unit segmentation are not only an too laborious for most of the Japanese people who are not accustomed in segmenting each sentence into words but also apt to be erroneous. It is, therefore, necessary in Kana-KanJi translation system to segment the Kana strings into words or other units automatically.",
"cite_spans": [
{
"start": 59,
"end": 62,
"text": "(i)",
"ref_id": null
}
],
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"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
},
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"text": "The number of different syllables in Japanese is much less than in English or in Chinese, while the number of KanJi is much more. Consequently, there are many groups of KanJi which have the same pronunciation. This fact makes word identification more difficult in Kana-KanJi translation since there is no one-to-one correspondence between KanJi and Kana. For example, Kana strings '= ~ ~ y'corresponds to 25 words in an ordinary dictionary and a part of these are shown below.",
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"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
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{
"text": "Example.",
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"eq_spans": [],
"section": "The problem 9 iP Kana-Kap~i translation",
"sec_num": null
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{
"text": "KanJi a meaning ~ a battle ~ a resistance ~ an iron ship ~ a bea.",
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"section": "Kana",
"sec_num": null
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{
"text": "The segmentation process Bunsetsu A Japanese sentence is composed of the sequences of syntactic units called Bunsetsu pronounced without pausing. Bunsetsu usually consists of two parts: an independent part and a dependent part. The independent part consists of an independent word or its derivative, and the dependent part consists of a sequence of dependent words, given as follows:",
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"section": "~ a public election H~ a commission ~ a mineral spring",
"sec_num": null
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{
"text": "Bunsetsu=(independent part).(dependent part) independent part =[prefix].(independent word).",
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"section": "~ a public election H~ a commission ~ a mineral spring",
"sec_num": null
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"text": "[suffix] dependent part =[dependent word]* independent word=noun/pronoun/adverbs/ verb/adjective/verbal adjective/ attributive/conjuction/interjection dependent word=auxiliary verb/particle or postposition Here, brackets indicate optionality, the asterisk indicates one or more repititions or nonexisting and the slants indicate alternatives. The independent words('Jiritsugo') are divided into two main groups: inflected words which consist of verbs, adjectives and verbal adjectives('keiyodoshi'), and non-inflected words which consist of nouns, pronouns and others. On the other hands the dependent words consist of particles and auxiliary verbs which have their inflections.",
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"section": "~ a public election H~ a commission ~ a mineral spring",
"sec_num": null
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"text": "There are grammatical connectabilities between a preceding word and its succeeding word in Bunsetsu. This is explained using an example in Fig.2 .",
"cite_spans": [],
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{
"start": 139,
"end": 144,
"text": "Fig.2",
"ref_id": "FIGREF1"
}
],
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"section": "~ a public election H~ a commission ~ a mineral spring",
"sec_num": null
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{
"text": "(had to go) V AUX P AUX AUX AUX V:verbs, AUX:auxiliary verb, P:particle An indicative form 'ika' of a verb 'iku' can be concatenated not only by inflectional form 'nakere' of auxiliary verb 'nai' in this example but also by all of inflectional forms of 'nai'. And the particle 'ba' is preceded by the conditional form of 'nai'. Thus, these properties are decided upon each inflectional form of the preceding word(if the word is an inflected word) and its succeeding word. These connectability features in Bunsetsu constitute the basis of the segmentation of Kana strings described in later sections.",
"cite_spans": [],
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"eq_spans": [],
"section": "ikanakerebanaranakatta",
"sec_num": null
},
{
"text": "The lonsest string-match method of two Bunsetsu For segmentation, each independent word is, in the order of length, first separated by comparing the Kana strings with the vocabulary of a word dictionary, and is stored with the informations such as parts of speech and inflectional forms if necessary for further morhological analysis.",
"cite_spans": [],
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"section": "ikanakerebanaranakatta",
"sec_num": null
},
{
"text": "Then, the dependent words in the rest of the strings are recognized using the dependent-word list and grammatical connectabilities between the dependent word and the independent word are examined. This analysis is continued until no succeeding word is found in the successive Kana strings. Thus, the candidates of a Bunsetsu are extracted from Kana strings as below. The same analysis as mentioned above is executed for the rest of the strings from which each candidate of Bunsetsu is separated.",
"cite_spans": [],
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"section": "ikanakerebanaranakatta",
"sec_num": null
},
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"text": "Consequently, the sequence of two candidates of Bunsetsu is extracted from Kana strings, and then the Bunsetsu in the sentence is appropriately identified so as to make the total length of two consecutive strings of their candidates maximum. This algorithm decides only the boundary between two consecutive Bunsetsu. In other words, the preceding Kana strings and these constituents for the Bunsetsu are recognized. On the other hand, the decisions for succeeding Bunsetsu are tentative at this stage.",
"cite_spans": [],
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"section": "ikanakerebanaranakatta",
"sec_num": null
},
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"text": "These processes named as the longest stringmatch method of two Bunsetsu 4 are executed sentence by sentence and at length the input sentences are converted into Bunsetsu and homonyms in Bunsetsu are stored. An example is illustrated in Fig.3 The successive candidates of Bunsetsu in i) and 3) are compared since the succeeding Kana strings are not analyzed in 2). As the total length of two analyzed strings in i) is longer than that in 3), the segmentation in i), namely the Bunsetsu 'souiu' is decided as the result.",
"cite_spans": [],
"ref_spans": [
{
"start": 236,
"end": 241,
"text": "Fig.3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "ikanakerebanaranakatta",
"sec_num": null
},
{
"text": ". souiuzasshiwo... i) souiu zasshiwo... 2) soul... 3) soui iu...",
"cite_spans": [],
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"section": "ikanakerebanaranakatta",
"sec_num": null
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"text": "The longest string-match method of two Bunsetsu is based on the grammatical characterristies of the words, and so is not applicable to unknown words to the word dictionary. Hence, it would be easily expected that the appearance of an unknown word in a sentence makes the segmentation impossible. Therefore, it is necessary in non-segmented sentences to take account of the processing of unknown words.",
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"section": "The proccessin5 of unknown words",
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"text": "The dependent words are divided into two main groups by their connectability characteristics. One is the word class, named is A, that is preceded by nouns or non-inflected words. The other is the word class that is preceded by inflected words and is further sub-divided into four sub-classes, named as B, C, D and E, according to the preceding word conjugations which are of indefinite form, conjunction form, final form and conditional form, repectively. The dependent words and their classes of connectabilities are given in Table i . ",
"cite_spans": [],
"ref_spans": [
{
"start": 527,
"end": 534,
"text": "Table i",
"ref_id": "TABREF1"
}
],
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"section": "The proccessin5 of unknown words",
"sec_num": null
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"text": "A B A A C A A C C B C D A D C A A A",
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"section": "The proccessin5 of unknown words",
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"text": "Now, suppose that the search for the word dictionary fails. Then, the word in the above dependent word list is searched for the rest of the strings without being segmented. If a dependent word is found and its preceding Kana corresponds to an inflected word-ending succeeded by it vowels of inflectional endings of indefinite, conjunction, final and conditional forms are '-a', '-i' or '-e', '-u' and '-e', respectively, then the dependent word is recognized and its succeeding Kana strings are analyzed morphologically as mentioned in the preceding section. Consequently, the dependent word sequences are extracted and utilized for next segmentation.",
"cite_spans": [],
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"section": "The proccessin5 of unknown words",
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"text": "As mentioned above, a part of words in input sentences is identified in grammatical or.morphological analysis, But there are still many homonyms which have the same grammatical characteristics in general. Therefore, further word identification will need for syntactical and semantical analyses in a given sentence.",
"cite_spans": [],
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"section": "The word identification process among homon~L~",
"sec_num": null
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"text": "The usage dictionary contains the informations of word uses which play an important role on word identification from homonyms.",
"cite_spans": [],
"ref_spans": [],
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"section": "The usage dictionary",
"sec_num": null
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"text": "Informations of word uses would be divided into two groups: colloqual information of words such as derivatives, compound words and ideoms, and semantic informations such as \"semantic pattern\" representative of nouns and verbs.",
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"section": "The usage dictionary",
"sec_num": null
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"text": "Case relations accompanied with verbs in a sentence are explicitly marked with particles attached by nouns. Usually, the particles 'ga', 'wo' and 'ni' indicate nominative, objective and dative respectively, whose case relations are fundamental, and so these are called 'ga' case, 'wo' case and 'ni' case, respectively. Accordingly, the so-called case frame of each verb has been studied with an emphasis on these particles.",
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"section": "The usage dictionary",
"sec_num": null
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"text": "Example.",
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"section": "The usage dictionary",
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"text": "[ where, Ix] means a semantic feature or semantic category of x. One of difficulties of doing the work is the semantic classification of each word. To avoid this burden, the semantic category of each word is identified according to the system of \"The Word List by Semantic Principles\" edited by the National Language Research Institute, in which about 32,600 words are divided into 798 semantic categories. 5 The particle 'ni' also occurs after locative noun which mean the location. However, it is empirically assumed that either locative nouns or dative nouns occur with each verb in a simple sentence. The example is given as follows, The case frame 6 of each verb is different, and so semantic categories of nouns and standard particles used as semantical \"identifiers\" are described in the usage dictionary. Fig.4 A part of the usage dictionary The parsing After segmenting sentences into Bunsetsu, the parsing phase begins, in order not to take out so-called tree structures but to extract the syntactic relations between Bunsetsu or words. The parsing of the sentence is executed on the basis of the Kskariuke relations(something like the dependency relations) between Bunsetsu. The Kakariuke is the term in Japanese traditional school grammar.",
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"start": 407,
"end": 408,
"text": "5",
"ref_id": "BIBREF4"
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"start": 813,
"end": 818,
"text": "Fig.4",
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"section": "The usage dictionary",
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"text": "Bunsetsu decides what kinds of words to modify, on the other hand each of the independent words decides how to be modified. (2) Each Bunsetsu as a dependent always appears before its governor in a sentence. (3) Kakariuke relations between any two Bunsetsu do not cross with each other in a sentence.",
"cite_spans": [],
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"eq_spans": [],
"section": "Characteristics of Kakariuke relations in a sentence are given as follows: (i) A final word or an inflectional form in a",
"sec_num": null
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"text": "For simplicity of the parsing, we adopted the following two assumptions that would be correct in most sentences.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Characteristics of Kakariuke relations in a sentence are given as follows: (i) A final word or an inflectional form in a",
"sec_num": null
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"text": "Bunsetsu appearing after it except the Bunsetsu at the end of a sentence.",
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"section": "(4) A Kakariuke relation is decided on the smallest distance between a dependent and its probable governors. (5) Each Bunsetsu can be a dependent of only one",
"sec_num": null
},
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"text": "The relations among Bunsetsu are searched taking account of the following three factors: five conditions mentioned above, final word as a dependent and an independent word class as a governor. The term noun phrase is used for Bunsetsu in which an independent part is a noun, and similarly a verb phrase for Bunsetsu consisting of a verb and its dependent part. But, for the phrase of the form of a noun and some of auxiliary verbs, which are called as copulas ('desu', 'da' etc.), it is necessary to regard the phrase as a predicate in a sentence. Example kano~o ~ / watashi 7-n\u00b0 / musume~ des~ (She is my daughter. )",
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"section": "(4) A Kakariuke relation is decided on the smallest distance between a dependent and its probable governors. (5) Each Bunsetsu can be a dependent of only one",
"sec_num": null
},
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"text": "In the above example, an underline denotes a word and a slant does a segmental symbol between Bunsetsu. An arrowed line denotes the Kakariuke relation between Bunsetsu. Usually, the Kakariuke relation between Bunsetsu, 'watashino' and 'musamedesu', is determined by the particle 'no' and the noun 'musume', on the other hand the relation between 'kanojowa' and 'musumedesu' is determined by the particle 'ha' and the auxiliary verb 'desu'.",
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"section": "(4) A Kakariuke relation is decided on the smallest distance between a dependent and its probable governors. (5) Each Bunsetsu can be a dependent of only one",
"sec_num": null
},
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"text": "In Japanese, the different semantic relations are reduced to the same syntactic relations of verbs with nouns intermediated by particles in active voice as in passive voice. The passive or causative voice is represented explicitly by the attachment of auxiliary verbs ('reru, rareru') or auxiliary verbs('seru, saseru') to inflectional forms of verbs. Accordingly, the semantic normalization is necessary in the cases below. (i) where Ni, N2 and N3 denotes a noun and V denotes a transitive verb. The auxiliary verbs (reru and seru) are used for the consonant conjugation verbs(godan katsuyo doshi), on the other hand the auxiliary verbs(rareru and saseru) for the vowel conjugation verbs(ichidan katsuyo doshi). The meaning of independent part which consists of an independent and a suffix is substituted for the meaning of its suffix. Similarly, the meaning of the numbor that consists of the set of the numeral plus counter is representative of the meaning of its counter.",
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"start": 425,
"end": 428,
"text": "(i)",
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"section": "The pre-processin6 for the word identification",
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"text": "[nihon+jin] ---~[Jin] [lO0+nin] __,[nin]",
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"section": "Example",
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"text": "where,'jin'and'nin' are a suffix and a counter, respectively that mean the word \"human\". The dependent part composed of more than two dependent words are substituted for a dependent word representing a case in order to consult the usage dictionary in next steps. Word selections from homonyms Word selections from homonyms an executed using both colloqual informations and informations about cases with verbs.",
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"section": "Example",
"sec_num": null
},
{
"text": "Word selections from homonyms are executed particle attached to each noun. At that time, each particle is converted into the \"standard particle\" in the preprocessing phase. And so, each semantic category of homonyms (nouns) is compared with the corresponding semantic category code in the usage dictionary, and the most matched word is selected. When homonyms are verbs, the verb and the nouns as case elements of the verb are selected taking account of the numbers of case found in the sentence. The nouns related with verbs intermediated by the particle 'no' are referredto the nominative nouns. As it is assumed that the noun attached by copulas such as 'desu' are in the synonymous relation to nominative nouns, each pair is selected from homonyms.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word selection based on noun-to-verb relation",
"sec_num": null
},
{
"text": "As it is difficult to estimate the case relations between verbs and nouns modified by their verbs because of no occurrence of particles, the reference to the case elements not identified yet are tried. In the example below, the words 'hon' are examined whether they are nominative or objective elements of the verb 'morau'. For the Bunsetsu composed of prefixes and/or suffixes and independent words, the derivative is decided according to their prefixes and suffixes in the usage dictionary.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word selection based on noun-to-verb relation",
"sec_num": null
},
{
"text": "When the successive nouns are found, each registration is examined, and the registered word in the usage dictionary is selected if any.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word selection based on noun-to-verb relation",
"sec_num": null
},
{
"text": "Informations as for idioms are also, referred for nouns and verbs in the Kakariuke relation because ~heir words are identified in colloqual expressions. In the sequence of two nouns, either of which is 'sahenmeishi', it is often assumed that the semantical relation between two nouns is based on the case relation because 'sahenmeishi' also have the characteristics as verbs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word selection based on noun-to-verb relation",
"sec_num": null
},
{
"text": "Jouhou wo shorisuru (... process informations...)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Example jouhou shori (information processing)",
"sec_num": null
},
{
"text": "The semantic category of alternative nouns 'jouhou' are compared with semantic categories of case elements of a verb 'shori + suru' are so \"~$~' is selected from homonyms(-kJY~J~, etc. )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Example jouhou shori (information processing)",
"sec_num": null
},
{
"text": "As it is assumed that two nouns intermediated by the conjunctive particles('to', 'ya', 'dano', 'nari', etc.) are in the relation of the same or similar semantic categories.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Example jouhou shori (information processing)",
"sec_num": null
},
{
"text": "The pair of nouns is selected, whose semantic category codes are close to each other. A synonym and antonym are included in the same semantic category as shown in the following example.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Example jouhou shori (information processing)",
"sec_num": null
},
{
"text": "The most frequent word is selected for homonyms undetermined by the analysis of word uses.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sensei to reiju ( absolutism and slavery )",
"sec_num": null
},
{
"text": "The dictionaries for this Kana-Kanji translation system are given in Table. 2 with a brief explanation.",
"cite_spans": [],
"ref_spans": [
{
"start": 69,
"end": 75,
"text": "Table.",
"ref_id": null
}
],
"eq_spans": [],
"section": "Dictionaries Implementatio,n -",
"sec_num": null
},
{
"text": "(a) The independent word dictionary The contents consist of sequential numbers, indexes of Kana, Kanji representation, numbers of Kanji, inflectional forms, word frequency, semantic category and information for dictionary search.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dictionaries Implementatio,n -",
"sec_num": null
},
{
"text": "This dictionary has about 8000 independent words chosen from \"Vocabulary and Chinese Characters in Ninety Magazines of Today. ''7",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dictionaries Implementatio,n -",
"sec_num": null
},
{
"text": "The connectability between preceding words and succeeding words in Bunsetsu is represented by the matrix, in which each row corresponds to the preceding words or their conjugations and each column to the succeeding words. Each element takes the value of i or 0, and i stands for that words of row are connectable to the succeeding words of the column.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "(b) Connection matrix",
"sec_num": null
},
{
"text": "The size of this matrix is 154X108.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "(b) Connection matrix",
"sec_num": null
},
{
"text": "(c) The table of inflectional word endings For analyzing three inflected words(verbs, adjective and verbal adjectives), their conjugations and their correspondences to each row of connection matrix are listed, because these occur before dependent words in Bunsetsu.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "(b) Connection matrix",
"sec_num": null
},
{
"text": "This list consists of dependent word (particles and inflectional forms of auxiliary verbs) and their correspondence of rows and columns of the connection matrix.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "(d) The dependent word list",
"sec_num": null
},
{
"text": "(e) The prefix, the suffix and the counter dictionaries These dictionaries include 47 prefixes, 311 suffixes and 141 counters, respectively, and also their Kanji representations. Moreover, the suffix and the counter dictionaries include their semantic category codes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "(d) The dependent word list",
"sec_num": null
},
{
"text": "The dependent list consists of the words and their classes listed in Table i .",
"cite_spans": [],
"ref_spans": [
{
"start": 69,
"end": 76,
"text": "Table i",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "(f) The dependent list for segmentation",
"sec_num": null
},
{
"text": "This dictionary have contents such as in Fig.2 . Results of the experiment is shown in Table 3 . Segmentation errors are divided into errors caused by the longest string-match method of two Bunsetsu, unknown word and grammatical incompleteness, whose examples are denoted at (i), (2) and (3) in Table 4 , respectively.",
"cite_spans": [
{
"start": 280,
"end": 283,
"text": "(2)",
"ref_id": "BIBREF1"
},
{
"start": 288,
"end": 291,
"text": "(3)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [
{
"start": 41,
"end": 46,
"text": "Fig.2",
"ref_id": "FIGREF1"
},
{
"start": 87,
"end": 94,
"text": "Table 3",
"ref_id": "TABREF6"
},
{
"start": 295,
"end": 302,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "(g) The usage dictionary",
"sec_num": null
},
{
"text": "Errors by the longest string-match method of two Bunsetsu occurred on seven boundaries of Bunsetsu in the data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "(g) The usage dictionary",
"sec_num": null
},
{
"text": "On the other hand, word selection errors are apparently due to the uses of word frequencies. However, the true causes of errors are due to incompleteness of homonym analysis. They are given as follows; not taking account of the segmentical relation underlying between nouns formed with the noun phrase pattern\"noun + 'no' + noun\", not identifying the meaning of pronoun in context, not identifying the ambiguities between case relations and other semantic relations, for example, such as adverbial relation for verbs, Their examples in the data are illustrated in (4), (5) and (6) of Table 4 , respectively. Appendix shows examples of the segmented sentences and the corresponding sentences in Kanji and Kana. ",
"cite_spans": [
{
"start": 569,
"end": 572,
"text": "(5)",
"ref_id": "BIBREF4"
},
{
"start": 577,
"end": 580,
"text": "(6)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 584,
"end": 591,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "(g) The usage dictionary",
"sec_num": null
},
{
"text": "We have proposed new approach for two main problems: segmentation of sentences into Bunsetsu and homonym analysis, in automatic Kana-Kanji translation, which should be basic linguistic problems. Moreover, an experimental system was constructed to make sure of their efficiency. As a result of experiments 96.2 per cent of the whole Bunsetsu in input sentences were seccessfully translated into Kanji where they should be.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": null
},
{
"text": "For promoting applicabilities of this system, we are going to prepare the dictionary including about 30,000 words in daily use.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": null
},
{
"text": "The difficulties in Kana-Kanji translation is based on ambiguities about the utterance, accordingly, further studies on understanding sentences would be needed for overcoming these difficultes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": null
}
],
"back_matter": [
{
"text": "We would like to thank Mr. Masakazu Okada for his cooperation in this work.The research described in this paper was partially supported by the Ministry of Education Science and Culture in 1979.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknpwled@ements",
"sec_num": null
},
{
"text": "An input sentence is first segmented in Bunsetsu, and second Kana homonyms in Bunsetsu are identified, consequently transformed into Kanji and Kana sentence. These processes are executed alternatively in a sentence as illustrated in Fig ",
"cite_spans": [],
"ref_spans": [
{
"start": 233,
"end": 236,
"text": "Fig",
"ref_id": null
}
],
"eq_spans": [],
"section": "annex",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Machine Translation System of 'Kana' Presentations to 'Kanji-Kana' Mixed Presentations",
"authors": [
{
"first": "I",
"middle": [],
"last": "Aizawa",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Ebara",
"suffix": ""
}
],
"year": 1973,
"venue": "NHK. Tech. Res",
"volume": "",
"issue": "",
"pages": "261--98",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "I. Aizawa and T. Ebara, \"Machine Trans- lation System of 'Kana' Presentations to 'Kanji-Kana' Mixed Presentations.\" NHK. Tech. Res., pp. 261-98(1973).",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Kana Alphabet to Kanji Converting System",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Matsushita",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Yamazaki",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Sato",
"suffix": ""
}
],
"year": 1974,
"venue": "JOHOSHORI",
"volume": "15",
"issue": "i",
"pages": "2--9",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y. Matsushita, H. Yamazaki and F. Sato, \"Kana Alphabet to Kanji Converting System.\" JOHOSHORI, Vol. 15, No. i, pp. 2-9(1974).",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Transformation of Kana-input into Kanji-presented Sentence",
"authors": [
{
"first": "H",
"middle": [],
"last": "Makino",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Kizawa",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Katsube",
"suffix": ""
}
],
"year": 1977,
"venue": "JOHOSHORI",
"volume": "18",
"issue": "7",
"pages": "656--63",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Makino, M. Kizawa and Y. Katsube, \"Trans- formation of Kana-input into Kanji-presented Sentence.\" JOHOSHORI. Vol. 18. No. 7, PP. 656-63(1977).",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Automatic Segmentation for Transformation of Kana into Kanji",
"authors": [
{
"first": "H",
"middle": [],
"last": "Makino",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Kizawa",
"suffix": ""
}
],
"year": 1979,
"venue": "Trans. of Inf. Proc. Society of Japan",
"volume": "20",
"issue": "4",
"pages": "337--382",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Makino and M. Kizawa, \"Automatic Segmen- tation for Transformation of Kana into Kanji\" Trans. of Inf. Proc. Society of Japan, Vol. 20. No. 4, pp. 337-45(1979).",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "The National Language Research Institute",
"authors": [],
"year": 1973,
"venue": "The Word List by Semantic Principles\" p. 362, SYUEI SYUPPAN",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "The National Language Research Institute, \"The Word List by Semantic Principles\" p. 362, SYUEI SYUPPAN, Tokyo, Japan (1973).",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "The Case For Case",
"authors": [
{
"first": "C",
"middle": [
"J"
],
"last": "Fillmore",
"suffix": ""
}
],
"year": 1968,
"venue": "Universals in Linguistic Theory",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "C. J. Fillmore, \"The Case For Case\" in Uni- versals in Linguistic Theory, Holt, Rinehart and Winston, New York (1968).",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Vocabulary and Chinese Characters in Ninety Magazines of Today",
"authors": [],
"year": 1962,
"venue": "SYUEI SYUPPAN",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "The National Language Research Institute, \"Vocabulary and Chinese Characters in Ninety Magazines of Today\" p. 321, SYUEI SYUPPAN, Tokyo, Japan (1962).",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"uris": null,
"text": "Examples of segmentations in a Japanese sentence.",
"num": null
},
"FIGREF1": {
"type_str": "figure",
"uris": null,
"text": "An example of Bunsetsu",
"num": null
},
"FIGREF2": {
"type_str": "figure",
"uris": null,
"text": "Segmentation process of Kana strings by the longest stringmatch method of two Bunsetsu.",
"num": null
},
"FIGREF3": {
"type_str": "figure",
"uris": null,
"text": "hito] ni [ie] ni itta ...said to the men to the house... ...went The above example is unusual and this fact means that semantic features of nouns with 'hi' are derived from surface structures of sentences.",
"num": null
},
"FIGREF4": {
"type_str": "figure",
"uris": null,
"text": "Example Tokyo.he.mo itta ~ Tokyo.he itta (went to Tokyo, too) (went to Tokyo)",
"num": null
},
"FIGREF5": {
"type_str": "figure",
"uris": null,
"text": "Kare ni moratta hon (book received from him ) ---r-m--rhon ga morau hon wo morau Word selection based on noun-to-noun relations",
"num": null
},
"FIGREF6": {
"type_str": "figure",
"uris": null,
"text": "(a) The independent dictionary (b) The connection matrix (c) The table of inflectional endings (d) The dependent word list (e) The prefix, the suffix and the counter dictionaries (f) The usage dictionaryThe systemThe automatic Kana-Kanji translation system was inplemented on FACOM 230-45S equipped with 256 kilobyte memory. The programs in PL/I consist of 17 sub-programs. are arranged in their frequency order in (I). Arrowed lines denote the Kakariuke relation between Bunsetsu.",
"num": null
},
"FIGREF7": {
"type_str": "figure",
"uris": null,
"text": "An example of Kana-Kanji translation process.",
"num": null
},
"TABREF1": {
"content": "<table><tr><td>words</td><td>class</td><td>words</td><td>class</td></tr><tr><td>no</td><td>A</td><td>ya</td><td/></tr><tr><td>ni</td><td>A</td><td>u</td><td/></tr><tr><td>te</td><td>C</td><td>nado</td><td/></tr><tr><td>wo</td><td>A</td><td>dake</td><td/></tr><tr><td>ha</td><td>A</td><td>ZU</td><td/></tr><tr><td>ta</td><td>C</td><td>demo</td><td/></tr><tr><td>ga</td><td>A</td><td>yori</td><td/></tr><tr><td>da</td><td>A</td><td>nagara</td><td/></tr><tr><td>de</td><td>A</td><td>tara</td><td/></tr><tr><td>to</td><td>A</td><td>n'</td><td/></tr><tr><td>mo</td><td>A</td><td>tari</td><td/></tr><tr><td>nai</td><td>B</td><td>shi</td><td/></tr><tr><td>masu</td><td>C</td><td>rashii</td><td/></tr><tr><td>kara</td><td>A</td><td>beki</td><td/></tr><tr><td>desu</td><td>A</td><td>naku</td><td/></tr><tr><td>he</td><td>A</td><td>bakari</td><td/></tr><tr><td>ka</td><td>A</td><td>shika</td><td/></tr><tr><td>ba</td><td>E</td><td>taru</td><td/></tr><tr><td>made</td><td>A</td><td/><td/></tr></table>",
"type_str": "table",
"num": null,
"html": null,
"text": "Classification on connectability of dependent words."
},
"TABREF5": {
"content": "<table/>",
"type_str": "table",
"num": null,
"html": null,
"text": "List of dictionaries"
},
"TABREF6": {
"content": "<table><tr><td/><td colspan=\"2\">Experimental result</td></tr><tr><td/><td>segmentation</td><td>translation</td></tr><tr><td>correct</td><td>98.8 %</td><td>96.2 %</td></tr><tr><td>error</td><td>1.2 %</td><td>3.8 %</td></tr><tr><td colspan=\"3\">Translation errors are classified into</td></tr><tr><td colspan=\"3\">segmentation errors and word selection errors.</td></tr></table>",
"type_str": "table",
"num": null,
"html": null,
"text": ""
},
"TABREF7": {
"content": "<table><tr><td>Correct</td></tr><tr><td>Iq \u00a9A#a</td></tr></table>",
"type_str": "table",
"num": null,
"html": null,
"text": ""
}
}
}
} |