| { |
| "paper_id": "P84-1049", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T08:20:58.780022Z" |
| }, |
| "title": "USE OF H~ru'RISTIC KN~L~EDGE IN CHINF-.SELANGUAGEANALYSIS", |
| "authors": [ |
| { |
| "first": "Yiming", |
| "middle": [], |
| "last": "Yang", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Kyoto University", |
| "location": { |
| "addrLine": "Sakyo-ku", |
| "postCode": "606", |
| "settlement": "Kyoto", |
| "country": "JAPAN" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Toyoaki", |
| "middle": [], |
| "last": "Nishida", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Kyoto University", |
| "location": { |
| "addrLine": "Sakyo-ku", |
| "postCode": "606", |
| "settlement": "Kyoto", |
| "country": "JAPAN" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Shuji", |
| "middle": [], |
| "last": "Doshita", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Kyoto University", |
| "location": { |
| "addrLine": "Sakyo-ku", |
| "postCode": "606", |
| "settlement": "Kyoto", |
| "country": "JAPAN" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "This paper describes an analysis method which uses heuristic knowledge to find local syntactic structures of Chinese sentences. We call it a preprocessing, because we use it before we do global syntactic structure analysisCl]of the input sentence. Our purpose is to guide the global analysis through the search space, to avoid unnecessary computation.", |
| "pdf_parse": { |
| "paper_id": "P84-1049", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "This paper describes an analysis method which uses heuristic knowledge to find local syntactic structures of Chinese sentences. We call it a preprocessing, because we use it before we do global syntactic structure analysisCl]of the input sentence. Our purpose is to guide the global analysis through the search space, to avoid unnecessary computation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "To realize this, we use a set of special words that appear in commonly used patterns in Chinese.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "We call them \"characteristic words\" . They enable us to pick out fragments that might figure in the syntactic structure of the sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Knowledge concerning the use of characteristic words enables us to rate alternative fragments, according to pattern statistics, fragment length, distance between characteristic words, and so on.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "The preprocessing system proposes to the global analysis level a most \"likely\" partial structure. In case this choice is rejected, backtracking looks for a second choice, and so on.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "For our system, we use 200 characteristic words.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Their rules are written by 101 automata. We tested them against 120 sentences taken from a Chinese physics text book.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "For this limited set, correct partial structures were proposed as first choice for 94% of sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Allowing a 2nd choice_, the score is 98%, with a 3rd choice, the score is 100%.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Being a language in which only characters ( ideograns ) are used, Chinese language has specific problems.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. THE PROBLEM OF CHINESE LANGUAGE ANALYSIS", |
| "sec_num": null |
| }, |
| { |
| "text": "Compared to languages such as English, there are few formal inflections to indicate the grammatical category of a word, and the few inflections that do exist are often omitted.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. THE PROBLEM OF CHINESE LANGUAGE ANALYSIS", |
| "sec_num": null |
| }, |
| { |
| "text": "In English, postfixes are often used to distinguish syntactical categories (e.g. translation, translate; difficul!, dificulty), but in Chinese it is very common to use the same word (characters) for a verb, a noun, an adjective, etc.. So the ambiguity of syntactic category of words is a big problem in Chinese analysis.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. THE PROBLEM OF CHINESE LANGUAGE ANALYSIS", |
| "sec_num": null |
| }, |
| { |
| "text": "In another exa~ole, in English, \"-ing\" is used to indicate a participle, or \"-ed\" can be used to distinguish passive mode from active. In Chinese, there is nothing to indicate participle, and although there is aword, \"~ \" , whose function is to indicate passive mode, it is often omitted. Thus for a verb occurring in a sentence, there is often no w~y of telling if it transitive or intransitive, active or passive, participle or predicate of the main sentence, so there may be many ambiguities in deciding the structure it occurs in.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. THE PROBLEM OF CHINESE LANGUAGE ANALYSIS", |
| "sec_num": null |
| }, |
| { |
| "text": "If we attempt Chinese language analysis using a conputer, and try to perform the syntactic analysis in a straightforward way, we run into a combinatorial explosion due to such ambiguities.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. THE PROBLEM OF CHINESE LANGUAGE ANALYSIS", |
| "sec_num": null |
| }, |
| { |
| "text": "What is lacking, therefore, is a simple method to decide syntactic structure.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. THE PROBLEM OF CHINESE LANGUAGE ANALYSIS", |
| "sec_num": null |
| }, |
| { |
| "text": "In the Chinese language, there is a kind of word (such as preposition, auxiliary verb, modifier verb, adverbial noun, etc..), that is used as an independant word (not an affix). They usually have key functions, they are not so numerous, their use is very frequent, and so they may be used to reduce anbiguities. Here we shall call them \"characteristic words\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "REDUCING AMBIGUITIES USING CHARACTERISTIC WORDS", |
| "sec_num": "2." |
| }, |
| { |
| "text": "Several hundreds of these words have been collected by linguists [2] ,and they are often used to distinguish the detailed meaning in each part of a Chinese sentence.", |
| "cite_spans": [ |
| { |
| "start": 65, |
| "end": 68, |
| "text": "[2]", |
| "ref_id": "BIBREF1" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "REDUCING AMBIGUITIES USING CHARACTERISTIC WORDS", |
| "sec_num": "2." |
| }, |
| { |
| "text": "Here we selected about 200 such words, and we use them to try to pick out fragments of the sentence and figure out their syntactic structure before we attempt global syntactic analysis and deep meaning analysis.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "REDUCING AMBIGUITIES USING CHARACTERISTIC WORDS", |
| "sec_num": "2." |
| }, |
| { |
| "text": "The use of the characteristic words is described below.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "REDUCING AMBIGUITIES USING CHARACTERISTIC WORDS", |
| "sec_num": "2." |
| }, |
| { |
| "text": "Some characteristic words may serve to decide the category of neighboring words. For example, words such as \"~ \", \"~\", \"~\", \"4~\", are rather like verb postfixes, indicating that the preceding word must be a verb, even though the same characters might spell a noun. Words like \" ~ \", \" ~ \", can be used as both verb and auxiliary.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "a) Category decision:", |
| "sec_num": null |
| }, |
| { |
| "text": "If, for example, \"~ \" is followed by a word that could be read as either a verb or a noun, then this word is a verb and \"~ \" is an auxiliary.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "a) Category decision:", |
| "sec_num": null |
| }, |
| { |
| "text": "In Chinese, many prepositional phrases start", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "I fl,PP o o x x f2, #vP o 0 x ~ ~f5, #VP o o o x x", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "Translation:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "\u00a9 o x", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "The ball must run a longer distance before returning to the initial altitude on this slope. with a preposition such as \"~\", \"~\", \"~\", and finish on a characteristic word belonging to a subset of adverbial nouns that are often used to express position, direction, etc..", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "When such characteristic words are spotted in a sentence, they serve to forecast a prepositional phrase. Another example is the pattern \"...{ ... ~\", used a little like \"... is to ...\" in English, so when we find it, we may predict a verbal phrase from \"~ \" to \"%.~\", that is in addition the predicate VP of the sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "These forecasts make it more likely for the subsequent analysis system to find the correct phrase early.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Fragment picking", |
| "sec_num": null |
| }, |
| { |
| "text": "The preceding rules are rather simple rules like a human might use.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Role deciding", |
| "sec_num": null |
| }, |
| { |
| "text": "With a cxmputer it is possible to use more ~lex rules (such as involving many exceptions or providing partial knowledge) with the same efficiency. For example, a rule can not usually with certainty decide if a given verb is the predicate of a sentence, but we know that a predicate is not likely to precede a characteristic word such as \"~9 \" or \" { \" or follow a word like \"~-~\", \"~\" or \"~\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Role deciding", |
| "sec_num": null |
| }, |
| { |
| "text": "We use this kind of rule to reduce the range of possible predicates.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Role deciding", |
| "sec_num": null |
| }, |
| { |
| "text": "This knowledge can be used in turn to predict the partial structure in a sentence, because the verbal proposition begins with the predicate and ends at the end of the sentence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Role deciding", |
| "sec_num": null |
| }, |
| { |
| "text": "In the example shown in Fig.l , fragments f3 and f4 are obtained through step (a) (see above), fl through (b), and f2 and f5 through (c).", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 24, |
| "end": 29, |
| "text": "Fig.l", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "c) Role deciding", |
| "sec_num": null |
| }, |
| { |
| "text": "The symbol \"o\" shows a possible predicate, and \"x\" means that the possibility has been ruled out. Out of 7 possibilities, only 2 remained.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Role deciding", |
| "sec_num": null |
| }, |
| { |
| "text": "The rules we mentioned above are written for each characteristic word independantly. They are not absolute rules, so when they are applied to a sentence, several fragments may overlap and thus be incrmpatible.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "RESOLVING CONFLICT", |
| "sec_num": "3." |
| }, |
| { |
| "text": "Several crmabinations of compatible fragments my exist, and frcm these we must choose the most \"likely\" one.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "RESOLVING CONFLICT", |
| "sec_num": "3." |
| }, |
| { |
| "text": "Instead of attempting to evaluate the likelihood of every combination, we use a scheme that gives different priority scores to each fragment, and thus constructs directly the \"hest\" combination. If this combination (partial structure) is rejected by subsequent analysis, back-tracking occurs and searches for the next possibility, and so on. Fig.2 shows an example involving conflicting fragments. We select f3 first because it has the highest priority.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 342, |
| "end": 347, |
| "text": "Fig.2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "RESOLVING CONFLICT", |
| "sec_num": "3." |
| }, |
| { |
| "text": "We find that f2 , f4 and f5 collide with f3, so only fl is then selected next. The resulting combination (fl,f3) is correct. Fig.3 shows the parsing result obtained by computer in our preprocessing subsystem.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 125, |
| "end": 130, |
| "text": "Fig.3", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "RESOLVING CONFLICT", |
| "sec_num": "3." |
| }, |
| { |
| "text": "In the preprocessing, we determine all the possible fragments that might occur in the sentence and involving the characteristic words. Then we give each one a measure of priority. This measure is a complex function, determined largely by trial and error.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "PRIORITY", |
| "sec_num": "4." |
| }, |
| { |
| "text": "It is calculated by the following principles: a) Kind of fragment Some kinds of fragments, for example, compound verbs involving \"~\", occur more often than others and are accordingly given higher priority : In the perfect situation without friction the object will keep moving with constant speed.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "PRIORITY", |
| "sec_num": "4." |
| }, |
| { |
| "text": ": fragment obtained by preprocessing subsystem : the names of fragments shown in Fig. 2 : the omitted part of the resultant structure tree (Fig.4) . We distinguish 26 kinds of fragments.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 81, |
| "end": 87, |
| "text": "Fig. 2", |
| "ref_id": null |
| }, |
| { |
| "start": 139, |
| "end": 146, |
| "text": "(Fig.4)", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "PRIORITY", |
| "sec_num": "4." |
| }, |
| { |
| "text": "We call \"precise\" a pattern that contains recognizable characteristic words or subpatterns, and imprecise a pattern that contains words we cannot recognize at this stage.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Preciseness", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, f3 of Fig.2 is more precise than fl, f2 or f4.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 19, |
| "end": 24, |
| "text": "Fig.2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "b) Preciseness", |
| "sec_num": null |
| }, |
| { |
| "text": "We put the more precise patterns on a higher priority level.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "b) Preciseness", |
| "sec_num": null |
| }, |
| { |
| "text": "Length is a useful parameter, but its effect on priority depends on the kind of fragment. Accordingly, a longer fragment gets higher priority in some cases, lower priority in other cases.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Fragment length", |
| "sec_num": null |
| }, |
| { |
| "text": "The actual rules are rather complex to state explicitly.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Fragment length", |
| "sec_num": null |
| }, |
| { |
| "text": "At present we use 7 levels of priority.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Fragment length", |
| "sec_num": null |
| }, |
| { |
| "text": "tried the method on a set of mere complex sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Fragment length", |
| "sec_num": null |
| }, |
| { |
| "text": "From the same textbook, out of 800 sentences containing prepositional phrases, 80 contained conflicts, involving 209 phrases. Of these conflicts, in our test 83% ware resolved at first choice, 90% at second choice, 98% at third choice.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "c) Fragment length", |
| "sec_num": null |
| }, |
| { |
| "text": "In this paper, we outlined a preprocessing technique for Chinese language analysis.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SUMMARY", |
| "sec_num": "6." |
| }, |
| { |
| "text": "Heuristic knowledge rules involving a limited set of characteristic words are used to forecast partial syntactic structure of sentences before global analysis, thus restricting the path through the search space in syntactic analysis. Comparative processing using knowledge about priority is introduced to resolve fragment conflict, and so we can obtain the correct result as early as possible.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SUMMARY", |
| "sec_num": "6." |
| }, |
| { |
| "text": "In conclusion, we expect this scheme to be useful for efficient analysis of a language such as Chinese that contains a lot of syntactic ambiguities.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SUMMARY", |
| "sec_num": "6." |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "We wish to thank the members of our laboratory for their help and fruitful discussions, and Dr. Alain de Cheveigne for help with the English.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ACKNOWLEDGMENTS", |
| "sec_num": null |
| }, |
| { |
| "text": "PREPROCESSING EFFICIENCYThe preprocessing system for chinese language mentioned in the paper is in the course of development and it is partly ~u~leted. The inputs are sentences separated into words (not consecutive sequences of characters). We use 200 characteristic words and have written the rules by I01 automata for ~ them.As a preliminary evaluation, we tested the system (partly by hand) against 120 sentences taken from a Chinese physics text book. Frem these 369 fragments were obtained, of which 122 ware in conflict. The result of preprocessing was correct at first choice ( no back-tracking ) in 94% of sentences. Allowing one back-tracking yeilded 98%, two backtrackings gave 100% correctness.In this limited set, few conflicting prepositional phrases appeared.To test the performance of our preprocessing in this case we", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "5.", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "A Study of a System for Analyzing Chinese Sentence, masters dissertation", |
| "authors": [ |
| { |
| "first": "Yiming", |
| "middle": [], |
| "last": "Yang", |
| "suffix": "" |
| } |
| ], |
| "year": 1982, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Yiming Yang: A Study of a System for Analyzing Chinese Sentence, masters dissertation, (1982)", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "~,\\~\", (800 Mandarin Chinese Words)", |
| "authors": [ |
| { |
| "first": "Shuxiang", |
| "middle": [], |
| "last": "Lu", |
| "suffix": "" |
| } |
| ], |
| "year": 1980, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Shuxiang Lu: \"~,\\~\", (800 Mandarin Chinese Words), Bejing, (1980)", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "uris": null, |
| "num": null, |
| "text": "the word can not he predicate of sentenceFig.iAn Example of Fragment Finding", |
| "type_str": "figure" |
| }, |
| "FIGREF1": { |
| "uris": null, |
| "num": null, |
| "text": "In the perfect situation -without friction the object will keep moving with constant speed.: pattern of fragment : a word which is either a verb or a noun (undetermined at this stage)", |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "uris": null, |
| "num": null, |
| "text": "Fig. 3 An Exan~le of The Analysing Result Obtained by The Preprocessing Subsystem", |
| "type_str": "figure" |
| }, |
| "TABREF0": { |
| "type_str": "table", |
| "text": "JD ........................... i ...................... ~--DODA .......... EN", |
| "html": null, |
| "content": "<table><tr><td/><td/><td/><td/><td>S</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td>I</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td>?</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td>I</td><td/><td/><td/><td/></tr><tr><td>I</td><td/><td/><td/><td/><td/><td>ro</td><td>I</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>#</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>I</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>DO3</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>I</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>III</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>I</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td colspan=\"2\">DO3 ....... FZDO</td><td/></tr><tr><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td/></tr><tr><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>7</td><td>14</td><td>&</td><td/></tr><tr><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>l</td><td>I</td><td/></tr><tr><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>15 ....</td><td>16</td></tr><tr><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td><td>I</td></tr><tr><td colspan=\"4\">AI4A MEI2YOU3 MO2CA1 DE4A LI3XIANG3</td><td>QING2KUANG4</td><td>XIA4A</td><td>/UN4DONG4</td><td colspan=\"2\">XIA4A QU4A</td></tr><tr><td colspan=\"3\">Translation</td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td>fl</td><td>, f3</td><td/><td/><td/><td/><td/><td/></tr></table>", |
| "num": null |
| } |
| } |
| } |
| } |
