Base_JSON/prefixC/json/C80/C80-1019.json

{
    "paper_id": "C80-1019",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T13:05:16.905702Z"
    },
    "title": "CONCEPTUAL TAXONOMY OF JAPANESE VERBS FOR UNDERSTANDING NATURAL LANGUAGE AND PICTURE PATTERNS",
    "authors": [
        {
            "first": "Naoyuki",
            "middle": [],
            "last": "Okada",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Oita University",
                "location": {
                    "postCode": "870-11",
                    "settlement": "Oita",
                    "country": "Japan"
                }
            },
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "This paper presents a taxonomy of \"matter concepts\" or concepts of verbs that play roles of governors in understanding natural language and picture patterns. For this taxonomy we associate natural language with real world picture patterns and analyze the meanings common to them. The analysis shows that matter concepts are divided into two large classes:\"simple matter concepts\" and \"non-simple matter concepts.\" Furthermore, the latter is divided into \"complex concepts\" and \"derivative concepts.\" About 4,700 matter concepts used in daily Japanese were actually classified according to the analysis. As a result of the classification about 1,200 basic matter concepts which cover the concepts of real world matter at a minimum were obtained. This classification was applied to a translation of picture pattern sequences into natural language.",
    "pdf_parse": {
        "paper_id": "C80-1019",
        "_pdf_hash": "",
        "abstract": [
            {
                "text": "This paper presents a taxonomy of \"matter concepts\" or concepts of verbs that play roles of governors in understanding natural language and picture patterns. For this taxonomy we associate natural language with real world picture patterns and analyze the meanings common to them. The analysis shows that matter concepts are divided into two large classes:\"simple matter concepts\" and \"non-simple matter concepts.\" Furthermore, the latter is divided into \"complex concepts\" and \"derivative concepts.\" About 4,700 matter concepts used in daily Japanese were actually classified according to the analysis. As a result of the classification about 1,200 basic matter concepts which cover the concepts of real world matter at a minimum were obtained. This classification was applied to a translation of picture pattern sequences into natural language.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Abstract",
                "sec_num": null
            }
        ],
        "body_text": [
            {
                "text": "As is generally known, the intellectual activities of human beings are very instructive in higher processing of natural language and picture patterns, especially real world picture patterns. There are three sides to intellectual activity: (i) Recognition and understanding. (2) Thinking und inference. (3) Expression and (intellectual) action.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "The system of concepts or knowledge plays an essentially important role in each activity. The base of the system is considered to be placed on those concepts formed by direct association with the real world, which are closely related with both syntactic and semantic structures of natural language. The aim of this paper is to make this system clear from the linguistic viewpoint.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "There are two linguistic approaches to the analysis of the system. One is the understanding of the outline of the whole system and the other is the detailed analysis of a small part of the system. Compilation of a thesaurus is considered of the former type. Thesauruses compiled so far, 4,5 however, are not sufficient for machine processing because of the following:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "1-3",
                "sec_num": null
            },
            {
                "text": "i. Abstraction processes of concepts As shown in Sect. 2.2, it is important to introduce abstraction processes or conceptuali-zation processes to the system not only for its systematic analysis but also for the \"understanding'of natural language and picture patterns. The processes are not taken into consideration in ordinary thesauruses.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "1-3",
                "sec_num": null
            },
            {
                "text": "To know semantic interrelation among words are indispensable for natural language processing. This information is not explicitly expressed in ordinary thesauruses.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Interrelation among concepts",
                "sec_num": "2."
            },
            {
                "text": "In machine processing it must be shown why a word is classified into such and such term. Ordinary thesauruses do not stress the criteria.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Criterion for classification",
                "sec_num": "3."
            },
            {
                "text": "Concepts of verbs are the core of the system from the linguistic viewpoint. We classify almost all concepts of verbs in daily Japanese by association of natural language with the real world, answering the above-mentioned problems. As for problem i, a working hierarchy along an abstraction process is constructed in the system As for problem 2, case frames are shown in \"simple matter concept,\" and connecting relations among elementary matter concepts are shown in \" non-simple matter concept.\" As for problem 3, an algorithm is introduced into the classification.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Criterion for classification",
                "sec_num": "3."
            },
            {
                "text": "Putting aside what the meaning of a picture pattern is, let's first discuss how it can be understood. When a picture pattern or picture pattern sequence is given, an infinite number of static or dynamic events can generally be observed within. Suppose that the meaning of each event is described in natural language--in fact, one can express almost all events in natural language apart from the question of efficiency__ these descriptive sentences will amount to an infinite number. An ordinary sentence is reduced into simple sentences, each of which is governed syntactically and semantically by a verb. Since there is a finite number of verbs in each language, the meanings of an infinite number of the events involved are roughly divided into the meanings of those verbs and their interrelations. Now, what is the meaning of picture patterns ? In the case of circuit diagrams or chemcal structural formulas, we can think of the se-",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Meaning Common to Natural Language and Picture Patterns",
                "sec_num": "2.1"
            },
            {
                "text": "mantics because they have signs and syntactic relations. In the case of real world picture patterns, however, there exists neither signs nor syntactic relations. Here we observe real world objects named by human beings. If we consider them something like signs, we can think of the syntax, and then the semantics, too. The meanings are common to natural language and picture patterns, although their syntactic structures differ largely from each other.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "127-",
                "sec_num": null
            },
            {
                "text": "In order to clarify the notions of interpretation and understanding, first, we propose a working hierarchy of knowledge along the abstraction process, as follows: ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Paradigms for Interpretation and Understand-k~",
                "sec_num": "2.2"
            },
            {
                "text": "Features extracted from raw data. Fig. 1 shows the hierarchy. \"Interpretation\" is considered as an association of the data at one level with another level. (Here input images are considered as level zero data.) Since the knowledge system has several levels and each level has many domains, interpretation is possible in many ways. If an interpretation is performed under a certain control system that specifies which level and which domain the input data should be associated with, it is called \" understanding.\"",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 34,
                        "end": 40,
                        "text": "Fig. 1",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Data of visual features",
                "sec_num": null
            },
            {
                "text": "As the level number increases, a level becomes higher because abstractions of concepts proceed. But, which is deeper, level 1 or level 5 ? In natural language understanding, input sentences will probably be interpreted initially at level 4 or 5, then the interpretation may descend to level i, where level 1 might be deeper than either level 4 or 5. However, if the interpretation of a picture pattern proceeds from level 1 to 5, we think level 5 as the deeper level.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "The knowledge system is so massive and complicated that it is necessary to make systematic analyses. Since the number of verbs are finite, concepts of verbs at level 4 provide a clue to systematic and exhaustive analyses of knowledge from the linguistic viewpoint.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "The concepts of verbs are divided into two large classes:\"simple matter concepts\" and \"nonsimple matter concepts.\" 2,3 (konoha-ga eda-kara) ochiru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "(A leaf) falls (from the branch). M2",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "(botan-ga shatu-kara) toreru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "(A button) comes off (the shirt).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "In M1 of eda(branch) is optional because ochiru is recognized by observing the vertical movement of a leaf, while in M2 of shatsu(shirt) is obligatory because toreru is not recognized without the existence of a shirt. Constituents of, ot, Ow, and o c belong to such a group.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data of conceptual features",
                "sec_num": null
            },
            {
                "text": "In case of semantic contents it is difficult to classify them by examining the combination of constituents, so we adopted a trial-and-error method extracting features for classification from the concepts. Letting a set of simple matter concepts under consideration be C, the feature extraction from \u00a3 is performed by the following recursive procedure:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "Step 1 Select several elements having similar contents from \u00a2 and extract from them a feature (~) which makes them similar.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "Step n(>2) Let the features extracted up to step (n-l) be Cl, c2,. .... ,Cn_ I. Extract a feature (c n) in the same way as step i. (The element so far selected may be adopted in the extraction.) And compare c n with each ci(l~i_<n-l). i) If c n is independent with each ci, adopt it as a feature and go to step (n+l). 2) Otherwise, 2.1) if the contents of Cn/C i contains that of ci/Cn, adopt c n as an upper/lower-grade feature of c i and go to step (n+l). 2.2) Otherwise, make c n as a special feature and go to step (n+l). ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "V VI VII VIII IX X XI v(s) v(s,of) v(s,ot) v(s ,o m) v(s,os) v(s,o) v(s,o,of) v(s,o,ot) iV(S,O,O m) v(s,o,ow) v(s,o,oc)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "Types of structural patterns Example (konoha-ga) ochiru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(A leaf) falls.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(otoko-ga ie-kara) deru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(A man) goes (out of the house).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(tar~-ga yu~inkyoku-ni) iku.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(Taro) goes (to the post office).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(torakku-ga basu-to) butsukaru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(A truck) collides (with a bus).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(ko-ga oya-ni) niru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(Children) resemble (their parents).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(hanako-ga ringo-o) taberu.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(Hanako) eats (an apple).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(untensyu-ga tsumini-o kurumakara) orosu.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(A driver) unloads (baggage from the car).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(aeito-ga kaban-ni kyb~kasyo-o) treru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(Pupils) put (textbooks into knapsacks).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(hikUshi-ga kanseit~-to shing~-o) kawasu.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(A pilot) exchanges (information with a control tower).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(hito-ga saji-de sate-o) suk~.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(One) scoops (sugar with a spoon).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(hito-ga soyokaze-o suzushiku) kanjiru.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "(Men) feel (a gentle breeze cool). X~ Others This method was applied to the set of concepts described in Sect. 3.1 and the result is tabulated in Table 2 . Here distribution was obtained by the classification of Chapter 5. In Table 2 , the first digit 0, 1 and 2 in the classification numbers roughly represent movement, change, and state, respectively.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 146,
                        "end": 153,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    },
                    {
                        "start": 226,
                        "end": 233,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    }
                ],
                "eq_spans": [],
                "section": "Semantic Contents",
                "sec_num": "3.2"
            },
            {
                "text": "Generally, non-simple matter concepts are so abstract in comparison with simple ones that it is hard to show a clear association of natural language with the real world. We emphasize the analysis of how they are composed of simple ones.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Non-Sim~le M~tter Concepts",
                "sec_num": "4"
            },
            {
                "text": "If two elementary matter concepts v i and vj(not necessarily simple ones) are connected according to one of the rules shown in Table 3 and the connected concept is expressed by a Japanese complex word of two verbs for v i and vj, it is called a '~ complex concept of A.\" The rules in Table 3 were obtained from the investigation of about 900 matter concepts which consist of two matter concepts and are expressed by a Japanese complex word.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 127,
                        "end": 134,
                        "text": "Table 3",
                        "ref_id": "TABREF5"
                    },
                    {
                        "start": 284,
                        "end": 291,
                        "text": "Table 3",
                        "ref_id": "TABREF5"
                    }
                ],
                "eq_spans": [],
                "section": "Complex Concept A",
                "sec_num": "4.1"
            },
            {
                "text": "In rule XXI.I, vj(deru) is an uppergrade concept of vi(af~reru) and contains the contents of vi. Rule XXI.I is concerned with the whole and a part of the same matter, while rule XXI.IIwith two different matters. The former is considered as a special case of the latter in which two matters coincide with each other.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept A",
                "sec_num": "4.1"
            },
            {
                "text": "Rule XXI and XXllare logical while rule XXI[I is linguistic. As \"cause\" is one of the constituents in (A) in Sect. 3.1, XXI may be considered as a part of XX~II .",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept A",
                "sec_num": "4.1"
            },
            {
                "text": "The semantic contents of complex concept A consists of the v i and vj contents and their connecting relation.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept A",
                "sec_num": "4.1"
            },
            {
                "text": "Complex concept B consists of several elementary matter concepts and is usually expressed by a Japanese simple word. However, no general rule can be found to connect elementary matter concepts, so a hierarchical analysis was made for a small number of complex concepts of B as shown in Fig. 2 From the diachronic point of view, there seems to be a reason why a complex concept of B is expressed by a simple word. The relation among elementary matter concepts can not well be expressed by enumerating each verb as in the case of complex concept A. When one is going to designate matter in the real world without the verb identifying it, one must utter several sen- \u2022 , U and --> :logical product, logical sum and implication.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 286,
                        "end": 292,
                        "text": "Fig. 2",
                        "ref_id": "FIGREF1"
                    }
                ],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "vx:There is no word to represent it. odoroki-\"iru\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "(be surprisedJ~nte~ shikari-\"tobasu\" fumi-\"hazusu\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "(step-\"take off\") toi-\"kaesu\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "(ask-\"return\") tabe-\"tsukeru\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "(eat-\"stick on\")",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "~uri-\"dasu\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "(rain-\"come out\")",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "ii-\"kakeru\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "(say-\"hang up\") suri-\"agaru\" tences. If such necessities often arise and the relationship is conceptualized, it will be efficient to give it a name.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "As for semantic contents, elementary matter concepts and their relationship form a surface contents. Approximately 1,000 complex concepts of B were investigated according to the feature extraction method in Sect. 3.2 and the result is tabulated in Table 5 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 248,
                        "end": 255,
                        "text": "Table 5",
                        "ref_id": "TABREF7"
                    }
                ],
                "eq_spans": [],
                "section": "Complex Concept B",
                "sec_num": "4.2"
            },
            {
                "text": "Some concepts possess a function of deriving a new concept by operating others. Matter concepts derived from operative concepts with both morphemic structures and derivative information as shown in Table 6 and 7 respectively are called \"derivative concepts.\" Table 7 was obtained from the investigation of about 700 matter concepts, most of which are expressed by a complex word and one concept is operative to the other. The derivative information is very similar to the modal information of auxiliary verbs, but it differs in that some matter concepts are operated upon and those operations are fixed.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 198,
                        "end": 205,
                        "text": "Table 6",
                        "ref_id": "TABREF8"
                    },
                    {
                        "start": 259,
                        "end": 266,
                        "text": "Table 7",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Derivative Concept",
                "sec_num": "4.3"
            },
            {
                "text": "In order to determine whether analyses in Chapter 3 and 4 are good or not, we classified about 4,700 basic matter concepts in daily Japanese, which are listed in \"Word List by Semantic ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Classification",
                "sec_num": "5"
            },
            {
                "text": "An algorithm is introduced into the classification, reffering Fig. 3 and 4 . The elements or members of Vx(x=T,U,...) are denoted by Vxi(i =1,2,.'.) and the sum and difference in the set theory are denoted by + and -, respectively.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 62,
                        "end": 74,
                        "text": "Fig. 3 and 4",
                        "ref_id": "FIGREF6"
                    }
                ],
                "eq_spans": [],
                "section": "Algorithm of Classificatioh",
                "sec_num": "5.1"
            },
            {
                "text": "For each VTi of VT, i.i) examine whether VTi functions with others or by itself. If it functions with others, then it is excluded from V T.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "i) Preprocessing",
                "sec_num": null
            },
            {
                "text": "Example. -ga~u; 1.2) examine whether there is VTh(h<i ) which has the same contents as VTi and is expressed by the same verb as V~i. If there is such VTh, VTi is excluded from VT.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "i) Preprocessing",
                "sec_num": null
            },
            {
                "text": "Let,s denote a class of concepts excluded by I.I) and 1.2) by Vp and let VU=VT-Vp.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "i) Preprocessing",
                "sec_num": null
            },
            {
                "text": "For each VUi of YU, 2.1) if VUi is expressed by a derivative word, it is classified as a member of term L in Table  6 . It is further classified in more detail according to Table 7 ; 2.2) if VUi is expressed by a complex word of two verbs and one of these verbs is affixal, then it is regarded as a member of term LI in Table 6 , and classified in more detail according to Table 7 ; 2.3) if VUi is expressed by neither a derivative word nor a complex word, but it is regarded as a member of one of the terms in Table 7 , it is classified into that term. At the same time, it is classified into term L~ in Table 6 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 109,
                        "end": 117,
                        "text": "Table  6",
                        "ref_id": "TABREF8"
                    },
                    {
                        "start": 173,
                        "end": 180,
                        "text": "Table 7",
                        "ref_id": null
                    },
                    {
                        "start": 320,
                        "end": 327,
                        "text": "Table 6",
                        "ref_id": "TABREF8"
                    },
                    {
                        "start": 373,
                        "end": 380,
                        "text": "Table 7",
                        "ref_id": null
                    },
                    {
                        "start": 511,
                        "end": 518,
                        "text": "Table 7",
                        "ref_id": null
                    },
                    {
                        "start": 605,
                        "end": 612,
                        "text": "Table 6",
                        "ref_id": "TABREF8"
                    }
                ],
                "eq_spans": [],
                "section": "2) Classification of derivative concepts",
                "sec_num": null
            },
            {
                "text": "Let this class of concepts thus obtained be VD.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "2) Classification of derivative concepts",
                "sec_num": null
            },
            {
                "text": "For each VUi(@VDj) of VU, if VUi is expressed by a complex word of two verbs and each concept functions by itself, it is considered as a complex concept of A and classified according to Table 3 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 186,
                        "end": 193,
                        "text": "Table 3",
                        "ref_id": "TABREF5"
                    }
                ],
                "eq_spans": [],
                "section": "3) Classification of complex concepts of A",
                "sec_num": null
            },
            {
                "text": "The class thus obtained is denoted by V A.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "3) Classification of complex concepts of A",
                "sec_num": null
            },
            {
                "text": "Classification of complex concepts of B For each VUi(~VDj,VAk) of VU, if its contents does not belong to any term in Table 2 , it is regarded as a complex concept of B. The class thus obtained is denoted by V B and subject to the following process:",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 117,
                        "end": 124,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    }
                ],
                "eq_spans": [],
                "section": "4)",
                "sec_num": null
            },
            {
                "text": "For each VBi , 4.1) examine its surface structure and classify it according to Table i;  4. 2) examine its surface contents and classify it according to Table 5 .",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 79,
                        "end": 91,
                        "text": "Table i;  4.",
                        "ref_id": "TABREF6"
                    },
                    {
                        "start": 153,
                        "end": 160,
                        "text": "Table 5",
                        "ref_id": "TABREF7"
                    }
                ],
                "eq_spans": [],
                "section": "4)",
                "sec_num": null
            },
            {
                "text": "Let V~=VD+VA+VB andVs=Vu-V ~.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "4)",
                "sec_num": null
            },
            {
                "text": "In class V S of simple matter concepts, if there is a group with similar contents, choose a concept as the standard, then classify the re-mainder as similar concepts.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "5) Classification of similar concepts",
                "sec_num": null
            },
            {
                "text": "Example. Korogeru(roll)~ korobu(roll), marobu(roll), etc. are similar concepts for standard concept korogaru(roll).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "5) Classification of similar concepts",
                "sec_num": null
            },
            {
                "text": "Counter-example. Saezuru(chirp), hoeru(bark ), unaru(roar), inanaku(neigh), etc. are not similar concepts for standard concept naku(cry).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "5) Classification of similar concepts",
                "sec_num": null
            },
            {
                "text": "Here, it is assumed that if a certain concept is a standard concept, it is not a similar concept for another standard one at the same time.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "5) Classification of similar concepts",
                "sec_num": null
            },
            {
                "text": "The class of similar concepts thus obtained is denoted as V s and let Vb=VS-V s.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "5) Classification of similar concepts",
                "sec_num": null
            },
            {
                "text": "For each Vbi of Vb, 6.1) examine its structural pattern and classify it according to Table i; 6.2) examine its semantic contents and classify it according to Table 2.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 85,
                        "end": 93,
                        "text": "Table i;",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "6) Classification of standard concepts",
                "sec_num": null
            },
            {
                "text": "In the above process 2) through 6), one concept can be classified into two or more terms if necessary.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "6) Classification of standard concepts",
                "sec_num": null
            },
            {
                "text": "First, let's discuss the relation among the obtained classes along the abstraction process. There are two kinds of abstraction processes: (i) extracting common features from concepts as follows; bulldog--+dog-~animal--~living thing--~thing, (ii) connecting several concepts to form a new concept as shown in complex concept B. From the latter viewpoint, the relation among classes is schematized as indicated in Fig. 5 .",
                "cite_spans": [
                    {
                        "start": 138,
                        "end": 141,
                        "text": "(i)",
                        "ref_id": null
                    }
                ],
                "ref_spans": [
                    {
                        "start": 412,
                        "end": 418,
                        "text": "Fig. 5",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Results and Discussion",
                "sec_num": "5.2"
            },
            {
                "text": "Simple matter concepts (V b) are regarded as the base of matter concepts in the sense that V b covers the concepts of real world matter at a minimum and every other matter concept is led from V b by a rule. Two simple matter concepts are connected by a rule and form a little bit abstract concept or complex concept of A. Several matter concepts are organized by a fairly complicated rule into a new abstract concept or complex concept of B. One of the elementary concepts in a complex concept of A changes its meaning diachronically and becomes a derivative operator. So, the system of Japanese verb concepts has its own nature--although it is a fact that a large part of the system is universal-and is not manipulated at one level.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Fig. 5 Relation among obtained classes",
                "sec_num": null
            },
            {
                "text": "Next, Table 8 indicates the distribution of all matter concepts. The minute distribution in ",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 6,
                        "end": 13,
                        "text": "Table 8",
                        "ref_id": "TABREF9"
                    }
                ],
                "eq_spans": [],
                "section": "Fig. 5 Relation among obtained classes",
                "sec_num": null
            },
            {
                "text": "Vp 485 V T = V U + Vp 4,740",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Fig. 5 Relation among obtained classes",
                "sec_num": null
            },
            {
                "text": "each class has been shown in Table 2 , 5 and 7, respectively. Table 8 is instructive in investigating the human competence in organizing the language system. For example, if class Yb is regarded as \"primitive\" concepts, number 1,209 of Vb does not side with Schank's classification, 9 but with Minsky's idea. 7 From Table 2 , 5 and 7, we can measure the degree of human concern about real world matter. For example, term 0.0 in Table 2 shows human beings are most interested in displacements of objects.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 29,
                        "end": 36,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    },
                    {
                        "start": 62,
                        "end": 69,
                        "text": "Table 8",
                        "ref_id": "TABREF9"
                    },
                    {
                        "start": 316,
                        "end": 323,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    },
                    {
                        "start": 428,
                        "end": 435,
                        "text": "Table 2",
                        "ref_id": "TABREF3"
                    }
                ],
                "eq_spans": [],
                "section": "Fig. 5 Relation among obtained classes",
                "sec_num": null
            },
            {
                "text": "Finally, we consider that every matter concept under consideration was classified satisfactorily supporting our analyses.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Fig. 5 Relation among obtained classes",
                "sec_num": null
            },
            {
                "text": "As an application of this taxonomy, system SUPP(Syvstem for --Understanding Picture Patterns) was constructed. 10-12 The overall system is shown in Fig. 6 . Fig. i . The rules are applied to picture pattern pairs called \"before-after\" frame pairs. 7",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 148,
                        "end": 154,
                        "text": "Fig. 6",
                        "ref_id": "FIGREF7"
                    },
                    {
                        "start": 157,
                        "end": 163,
                        "text": "Fig. i",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Translation of Picture Pattern Sequences into Natural Language",
                "sec_num": "6"
            },
            {
                "text": "The conceptual component contains conceptual features, concepts, and networks of concepts, which correspond to level 3 through 5 data, respectively. A matter concept is expressed by [v : ClC2.'.cldl(El)d2(E2)'..dm(Em)] (B) where each c i denotes a feature of matter itself and is associated with a syntactic rule mentioned above. Each dj( ) denotes the case or roll of a constitnent and must be filled by a specific instance or concept of constituent. Features Ej( =ejlej2...ejn ) specify the conditions its asslgnment must meet. A network is constructed among similar matter concepts.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Translation of Picture Pattern Sequences into Natural Language",
                "sec_num": "6"
            },
            {
                "text": "The linguistic component consists of dictionaries for the production of Japanese and English sentences. The thesaurus component contains all the classified concepts in Chapter 5 and supports the development of other components.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Translation of Picture Pattern Sequences into Natural Language",
                "sec_num": "6"
            },
            {
                "text": "A sequence of picture patterns, or two-dimensional line drawings(handwriting is allowed), is input at time t0,tl,...,tn. A picture pattern at ti(0~i~n-l) is paired with the one at ti+l, and processed as follows: i) Primitive picture recognition and syntactic analysis The picture pattern reader is a curve follower that traces line segments by octagonal scanning. The recognizer is based on Evans's matching program for graph-like line drawings but is improved to handle noisy ones. 13 The syntactic analyzer A decomposes the complex picture, in which two or more primitive pictures may intersect or touch each other, and recognizes them according to Gestalt criteria. The syntactic analyzer B performs Boolean operations on quantized primitive pictures to check such a relation as \"MAN INSIDE HOUSE.\" The syntactic analyzer C performs numerical operations on the data such as coordinates and transformational coefficients of primitive pictures.",
                "cite_spans": [
                    {
                        "start": 483,
                        "end": 485,
                        "text": "13",
                        "ref_id": "BIBREF12"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Translation Process",
                "sec_num": "6.2"
            },
            {
                "text": "The semantic analyzer detects the meaning of matter-centred change in picture pattern pairs by top-down analysis. Suppose that matter [v(s, oa): ClC2...Clds(es)doa(eo~)] is directed by the Inference. The analyzer assigns the role of s to one of the primitive pictures, say Ps, after checking whether Ps meets e s. It assigns the role of o~ to another primitive picture Poa in the same way. Then it analyzes each c i by calling a correspondent sub-program in the syntactic analyzer B or C. If all the analyses end in success, the meaning of v(s,oa) is detected, The present Inference makes inferences about all the similar concepts in the network in depth-first order, directing each matter concept at a node to the semantic analyzer.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "2) Semantic analysis and inference",
                "sec_num": null
            },
            {
                "text": "Finally, the synthesizer produces Japanese and English simple sentences.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "2) Semantic analysis and inference",
                "sec_num": null
            },
            {
                "text": "All the programs except the picture pattern reader are written in Fortran and run under the OS~/VS of the FACOM 230-38S medium scale computer at Oita University. Running with the syn-tactic analyzer B and C, the semantic analyzer occupies approximately 200K bytes of core. Memory usage for all the dictionaries except the thesaurus component amounts to approximately 90K bytes. ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Experiments",
                "sec_num": "6.3"
            }
        ],
        "back_matter": [
            {
                "text": "As for (i) it is important to enumerate all the readings of the input picture pattern pair. The output sentences in Fig. 8 shows SUPP understands to a fair degree the change in the input. As for (ii) the ability in detecting semantic anomaly is important. SUPP checks it by Ej in ( B) in Sect. 6.1, but a little bit anomalous sentence 5) or 7) is output because the constructed dictionary of matter concepts is slightly insufficient. As for (JJi) the ability in paraphrasing sentences is needed. Output sentences 4) through 7) are an analytical paraphrase of \"THE BIRD [i] PERCHES ON THE TREE\" although SUPP has no knowledge about \"perch.\"",
                "cite_spans": [
                    {
                        "start": 7,
                        "end": 10,
                        "text": "(i)",
                        "ref_id": null
                    },
                    {
                        "start": 569,
                        "end": 572,
                        "text": "[i]",
                        "ref_id": null
                    }
                ],
                "ref_spans": [
                    {
                        "start": 116,
                        "end": 122,
                        "text": "Fig. 8",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "annex",
                "sec_num": null
            },
            {
                "text": "A taxonomy of Japanese matter concepts has been described. It is summarized as follows:Simple This taxonomy has made clear the outline of the system of all matter concepts in daily Japanese, and by SUPP picture pattern understanding research has come closer to natural language understanding research.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusions",
                "sec_num": "7"
            },
            {
                "text": "The author started his investigation of Japanese matter concepts and the development of SUPP some ten years ago when he was at Kyushu University. The author wishes to express gratitude to Prof. T.Tamati of Kyushu University for his kind guidance and material support.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledsement",
                "sec_num": null
            }
        ],
        "bib_entries": {
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                "raw_text": "Okada, N. and Tamati, T.: Interpretation of the Meaning of Moving Picture Patterns and its Description in Natural Language---Semantic Analy- sis, Trans. IECE, Japan, J59-D , 5, p.331-338(May 1976).",
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        "ref_entries": {
            "FIGREF0": {
                "num": null,
                "uris": null,
                "text": "and events in the real world. Image-like data.",
                "type_str": "figure"
            },
            "FIGREF1": {
                "num": null,
                "uris": null,
                "text": "hierarchy of complex concepts of B",
                "type_str": "figure"
            },
            "FIGREF2": {
                "num": null,
                "uris": null,
                "text": "(=Pi)) has (something (=A)) )~ (P~ passes (A to someone (f=ie2)),~ (P2) has (A), 4~ (Pi) celebrates (P2),~ (Pi) thanks (P2),~ (Pi) respects (e2))~ 3 (P2) has money,~ (P2) passes (money to Pi), (Pi) has (money), ~ (P2) returns (A to el), ~ (e2) uses (A), and ~ (e2) keeps (A). Ill Pi is higher than P2 in grade, and [ill Pi = wholesaler and P2=salesman.",
                "type_str": "figure"
            },
            "FIGREF3": {
                "num": null,
                "uris": null,
                "text": "~ )i e~",
                "type_str": "figure"
            },
            "FIGREF5": {
                "num": null,
                "uris": null,
                "text": "print-\"go up\") ~omi~ \"t~su \"(read-\"pass through' ~ashi-\"togeru\" i (do-\"aceomplish\") nobori-\"tsumeru\" i (climb up-\"cream\") uri-\"kiru\" (sell-\"cut\") omoshiro-\"garu\"",
                "type_str": "figure"
            },
            "FIGREF6": {
                "num": null,
                "uris": null,
                "text": "of derivative concepts] [Classificati0nof complex concepts of A] lCl@ssification of complex concepts of B] ]Classif%cation of simiiar concepts I IClassification of ~tandard.c0ncepts I Procedure of classification \"V~~--~\u00a5 U V T : a set of concepts of under consideration Vp : a class of concepts excluded by preprocessing V V : a set of mutually different matter concepts V~ : a set of non-simple matter concepts \u00a5C : a set of complex concepts V A : a class of complex concepts of A ~B : a class of complex concepts of B VD : a class of derivative concepts V S : a set of simple matter concepts Vs : a class of similar matter concepts V b : a class of standard concepts Fig. 4 Relation among sets and classes Principles\" edited by National Language Research Institue in Japan. 4",
                "type_str": "figure"
            },
            "FIGREF7": {
                "num": null,
                "uris": null,
                "text": "\"~'~-'=2'~7~:'='T 'I~ Syntactic ~--,.-----, I I JMatter s ntactic anal met A :~,ll.!p_E!_e2_!\u00a3_ ......... ~ ......... , Organization of the system 6.1 Knowledge System The knowledge system consists of four components, visual, conceptual, linguistic and thesaurus. The visual component contains models of primitive pictures and syntactic rules, which correspond to level 2 data in",
                "type_str": "figure"
            },
            "FIGREF8": {
                "num": null,
                "uris": null,
                "text": "and 8 indicate an example of the recognition of a primitive picture and the translation of a picture pattern pair, respectively. It took 47 seconds to recognize \"bird[l]\" in Fig. 7 and 60 seconds to analyze and infer the meanings of matter after the recognition of primitive pictures in Fig. 8. Katz and Fodor pointed out the three problems of a semantic theory: (i) Semantic ambiguity, (ii) Semantic anomaly, and (i/) Paraphrase. q5], [P9:q14 ], [Pl9\"q~],\" [PS:q~], [P6:ql~], [Pl0:ql3 ]' [PlS:q7 ], [P5:q2 ]'tpll:ql2 ]' [P17: q8 J , [P12:q9 ] [Pl6:qlO], [Pl2:q9],[Plq:qll] Transformation and similarity Translation: (524.4, 483.7), scaling: 2.173 times, rotation: 2.290 radian, reflection: in Y -axis. Similarity: 0.718(<_1) Fig. 7 Recognition of \"bird[l]\" Input picture pattern pair i) TORI[I] GA UTSURU.",
                "type_str": "figure"
            },
            "TABREF0": {
                "html": null,
                "content": "<table><tr><td/><td/><td>Sym-</td></tr><tr><td/><td colspan=\"2\">bolic data associated with visual</td></tr><tr><td/><td colspan=\"2\">features. Some of them correspond to</td></tr><tr><td/><td colspan=\"2\">Chomsky's syntactic features in the</td></tr><tr><td/><td colspan=\"2\">lexicon. 6</td></tr><tr><td/><td colspan=\"2\">Concept data</td><td>Data obtained by or-</td></tr><tr><td/><td colspan=\"2\">ganizing conceptual features. Most</td></tr><tr><td/><td colspan=\"2\">data have names as words. In case of</td></tr><tr><td/><td colspan=\"2\">the verb they roughly correspond to</td></tr><tr><td/><td colspan=\"2\">Minsky,s surface semantic frames. 7</td></tr><tr><td/><td colspan=\"2\">Interconnected concept data</td><td>Net-</td></tr><tr><td/><td colspan=\"2\">works of concept data. A concept can</td></tr><tr><td/><td colspan=\"2\">be interconnected with other con-</td></tr><tr><td/><td colspan=\"2\">cepts from various viewpoints.</td></tr><tr><td/><td>Interconnected</td></tr><tr><td/><td>concept data</td><td>[</td></tr><tr><td/><td>Level 4</td></tr><tr><td/><td colspan=\"2\">[Data of conceptl</td></tr><tr><td/><td>Level 3</td></tr><tr><td/><td colspan=\"2\">Data of concep-]</td></tr><tr><td/><td>tual features</td></tr><tr><td/><td>Level 2</td></tr><tr><td/><td>Data of visual</td></tr><tr><td/><td>I features</td></tr><tr><td colspan=\"2\">003 Level 1</td></tr><tr><td>H~</td><td>Raw data</td></tr><tr><td/><td>(Level 0 ')]</td></tr><tr><td colspan=\"2\">~ ~ Lktion process]</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "Schank,s scripts can be regarded as one of this type. 8 Some networks have names as words."
            },
            "TABREF2": {
                "html": null,
                "content": "<table><tr><td>No. Pattern</td></tr><tr><td>I</td></tr><tr><td>Z</td></tr><tr><td>IV</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": ""
            },
            "TABREF3": {
                "html": null,
                "content": "<table><tr><td>of semantic contents</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": ""
            },
            "TABREF4": {
                "html": null,
                "content": "<table><tr><td/><td/><td colspan=\"2\">watasu*</td><td/></tr><tr><td/><td/><td>(pass)</td><td/><td/></tr><tr><td colspan=\"3\">yuzuru \u1e7d (hand over</td><td>x</td><td/></tr><tr><td>ataer</td><td/><td>x'x</td><td/><td/></tr><tr><td>(give)o/</td><td>uru</td><td>u okuru</td><td>kasu</td><td>azukeru</td></tr><tr><td colspan=\"2\">(sell)</td><td>(present)</td><td>(lend)</td><td>(deposit)</td></tr><tr><td/><td>orosu</td><td/><td colspan=\"2\">* Simple matter</td></tr><tr><td colspan=\"3\">(sell by wholesale)</td><td colspan=\"2\">concept</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "and Table 4."
            },
            "TABREF5": {
                "html": null,
                "content": "<table><tr><td/><td colspan=\"3\">Connecting rules of complex concept A</td></tr><tr><td/><td>Connecting</td><td>Example</td><td>Remark</td></tr><tr><td/><td>rule</td><td/><td/></tr><tr><td>XXI</td><td>cause and</td><td/><td/></tr><tr><td/><td>effect</td><td/><td/></tr><tr><td>XXI.I</td><td>Implication</td><td>(mizu-ga) afure-Jeru.</td><td>If water overflows,</td></tr><tr><td/><td/><td>(Water) overflow-comes out.</td><td>water comes out.</td></tr><tr><td>XXDK</td><td>Cause and effect</td><td>(dareka-ga watashi -o) oshiltaosu.</td><td>If someone ~ushes me,</td></tr><tr><td/><td/><td>(Some one) push-throws (me) down.</td><td>am thrown down.</td></tr><tr><td>XXK</td><td>Logical product</td><td>(sinja-ga) fushi-ogamu.</td><td>Believers kneel down</td></tr><tr><td/><td/><td>(Believers) kneel down-pray.</td><td>and pray.</td></tr><tr><td>XX]I[</td><td>Syntactic</td><td/><td/></tr><tr><td/><td>connection</td><td/><td/></tr><tr><td>XX ]]I.l</td><td>Relation between</td><td>(akago-ga) naki-yamu.</td><td>That a baby cries stops</td></tr><tr><td/><td>s and v</td><td/><td/></tr><tr><td>XX]I[']I</td><td>Relation between</td><td>(anauns~-ga genko-o) yomi-ayamaru.</td><td>An anouncer misses to</td></tr><tr><td/><td>o and v</td><td>(An anouncer) read-misses (his manus-cript).</td><td>read his manuscript.</td></tr><tr><td>XX]II.IK</td><td>Relation between o w and v</td><td>(kanshu-~a sh~jin-o) tatakz-okosu. (A ~uard) knock-awakes (prisoners).</td><td>A guard awakes \u2022 risoners y knockin~ tNem.</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "No."
            },
            "TABREF6": {
                "html": null,
                "content": "<table><tr><td>concept</td></tr><tr><td>yuzuru(hand over)</td></tr><tr><td>ataeru(give)</td></tr><tr><td>~u(sell)</td></tr><tr><td>orosu(sell by</td></tr><tr><td>wholesale)</td></tr><tr><td>oku~(present)</td></tr><tr><td>Vx</td></tr><tr><td>kasu (lend)</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": ""
            },
            "TABREF7": {
                "html": null,
                "content": "<table><tr><td>No.</td><td>Contents</td><td>Example</td><td>Dis</td></tr><tr><td>i0</td><td>Spiritual act</td><td/><td/></tr><tr><td>i0.0</td><td>Thought.recognition</td><td>mitomeru(recog-</td><td>35</td></tr><tr><td/><td/><td>nize)</td><td/></tr><tr><td>i0 .i</td><td>Guess.judgement</td><td>sassuru(guess)</td><td>25</td></tr><tr><td>i0.2</td><td>Respect.contempt</td><td>uyamau(respect)</td><td>18</td></tr><tr><td>i0.3</td><td>Haughty.flattery</td><td>hikerakasu(sport~</td><td>20</td></tr><tr><td>11</td><td>Academic and artistic</td><td/><td/></tr><tr><td/><td>act</td><td/><td/></tr><tr><td>ii \"0</td><td>Education.learning</td><td>oshieru(teach)</td><td>33</td></tr><tr><td>ii \"I</td><td>Creation</td><td>arawasu(write a</td><td>ii</td></tr><tr><td/><td/><td>book)</td><td/></tr><tr><td>12</td><td>Religious act</td><td/><td/></tr><tr><td>12 \"0</td><td>Belief</td><td>m$deru (visit a</td><td>16</td></tr><tr><td/><td/><td>temple or shirine</td><td/></tr><tr><td>12 .i</td><td>Celebration.marriage.</td><td>totsugu (marry)</td><td>16</td></tr><tr><td/><td>funeral</td><td/><td/></tr><tr><td>13</td><td>Verbal act</td><td/><td/></tr><tr><td>13 \"0</td><td>Praise.blame</td><td>homeru(praise)</td><td>12</td></tr><tr><td>13 .i</td><td>Instigation.banter</td><td>iodateru(insti-</td><td>12</td></tr><tr><td/><td/><td>igate)</td><td/></tr><tr><td>14</td><td>Social act</td><td/><td/></tr><tr><td>14 \"0</td><td>Life</td><td>i kurasu (live)</td><td>26</td></tr><tr><td>14 .i</td><td>Fostering</td><td>yashinau (bring up)</td><td>26</td></tr><tr><td>14.2</td><td>Antisocial. immoral</td><td>nusumu (steal)</td><td>43</td></tr><tr><td>14 \"3</td><td>Promise.negotiation</td><td>i suppokasu (breake</td><td>35</td></tr><tr><td/><td/><td>an appointment)</td><td/></tr><tr><td>15</td><td>Conduct.behavior</td><td>aumasu(assume a</td><td>25</td></tr><tr><td/><td/><td>prim air)</td><td/></tr><tr><td>16</td><td>Labour.production</td><td/><td/></tr><tr><td>16.0</td><td>Labour.work</td><td>tsutomeru(serve)</td><td>35</td></tr><tr><td>16 .i</td><td>Agriculture.industry commerce</td><td>akinau(deal in)</td><td>49</td></tr><tr><td>i7</td><td>Possesion</td><td/><td/></tr><tr><td>17.0</td><td>Owning.abandonement</td><td>y~suru(own)</td><td>ii</td></tr><tr><td>17 .i</td><td>Getting and giving. losing</td><td>ataeru(give)</td><td>55</td></tr><tr><td>17 -2</td><td>Selling and buying. lending and borrowing</td><td>kau (buy)</td><td>19</td></tr><tr><td>iS</td><td>Investigation.meas-urement</td><td/><td/></tr><tr><td>18.0</td><td>Investigation</td><td>shiraberu(inves-</td><td>24</td></tr><tr><td/><td/><td>tigate)</td><td/></tr><tr><td>18 .i</td><td>Measurement</td><td>hakaru(measure)</td><td>19</td></tr><tr><td>59</td><td>Domination.personal-affairs</td><td/><td/></tr><tr><td>19.0</td><td>Domination-obedience</td><td>suberu(dominate)</td><td>32</td></tr><tr><td>19 .i</td><td>Personal affairs</td><td>yatou(employ)</td><td>14</td></tr><tr><td>2O</td><td>Attack and defense</td><td/><td/></tr><tr><td/><td>victory and defeat</td><td/><td/></tr><tr><td>20.0</td><td>Attack and defense</td><td>semeru(attack)</td><td>26</td></tr><tr><td>20 .i</td><td>Victory and defeat-</td><td>makasu(defeat)</td><td/></tr><tr><td/><td>superiority and infe-</td><td/><td>19</td></tr><tr><td/><td>riority</td><td/><td/></tr><tr><td>21</td><td>Refuge.escape</td><td>nigeru(escape)</td><td>22</td></tr><tr><td>22</td><td>Rise and fall.pros-</td><td/><td/></tr><tr><td/><td>perity and decline</td><td/><td/></tr><tr><td colspan=\"2\">22.0 Rise and fall</td><td>horobosu(ruin)</td><td>Ii</td></tr><tr><td colspan=\"2\">22.1 Prosperity and de-</td><td colspan=\"2\">sakaeru(prosper) 19</td></tr><tr><td/><td>cline</td><td/><td/></tr><tr><td>23</td><td>Others</td><td>moyoosu(hold a</td><td>333</td></tr><tr><td/><td/><td>meeting)</td><td/></tr><tr><td/><td>Total</td><td colspan=\"2\">I~041</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "Surface contents of complex concept B"
            },
            "TABREF8": {
                "html": null,
                "content": "<table><tr><td/><td/><td/><td colspan=\"2\">operators</td></tr><tr><td>No.</td><td/><td/><td>Morpheme</td><td>Example</td><td>Remark</td></tr><tr><td>L</td><td/><td colspan=\"2\">Affix</td><td>kanashi-'garu '' be sad</td></tr><tr><td/><td/><td/><td/><td>(sad-\"garu \")</td></tr><tr><td>LI</td><td/><td colspan=\"3\">! Formative to</td></tr><tr><td/><td/><td/><td colspan=\"2\">conform affix</td></tr><tr><td colspan=\"2\">LI.I</td><td/><td>Prefixal</td><td>\"tor~\"chirakasu</td><td>scatter about</td></tr><tr><td/><td/><td/><td/><td>(\"take\"-scatter</td><td>awfully</td></tr><tr><td/><td/><td/><td/><td>about)</td></tr><tr><td colspan=\"3\">LI'III</td><td>Suffixal</td><td>akire-\"kaeru\"</td><td>be thoroughly</td></tr><tr><td/><td/><td/><td/><td>(he amazed-\"re-</td><td>amazed</td></tr><tr><td/><td/><td/><td/><td>turn\")</td></tr><tr><td>LI[</td><td/><td/><td>Others</td></tr><tr><td/><td/><td/><td>Table 7</td><td>Derivative information</td></tr><tr><td>No.</td><td/><td/><td colspan=\"2\">Derivative information</td><td>Example</td></tr><tr><td>50</td><td/><td/><td>Emphasis</td></tr><tr><td>50.0</td><td/><td/><td>Emphasis</td><td>\"tori\"-chirakasu</td></tr><tr><td/><td/><td/><td/><td>~'takdLscatter about)</td></tr><tr><td>50.1</td><td/><td/><td colspan=\"2\">Do completely</td></tr><tr><td>50.2</td><td/><td/><td colspan=\"2\">Do violently</td></tr><tr><td/><td/><td colspan=\"3\">Respect.politeness.</td></tr><tr><td/><td/><td colspan=\"2\">humbleness</td></tr><tr><td>52</td><td/><td colspan=\"2\">Vulgarity</td></tr><tr><td>53</td><td/><td colspan=\"3\">Poor practice.fai~</td></tr><tr><td/><td/><td colspan=\"2\">ure</td></tr><tr><td>53'0</td><td/><td colspan=\"3\">Be ill able to do</td></tr><tr><td>53\"1</td><td/><td colspan=\"3\">Lose a chance to do</td></tr><tr><td>53\"2</td><td/><td colspan=\"3\">Fail to do in part</td></tr><tr><td colspan=\"2\">53\"3!</td><td colspan=\"2\">Fail to do</td></tr><tr><td>54</td><td/><td colspan=\"3\">Repetition.habit</td></tr><tr><td colspan=\"2\">54\"01</td><td colspan=\"2\">Do again</td></tr><tr><td>54.1</td><td/><td colspan=\"3\">Be used to do</td></tr><tr><td>55</td><td/><td colspan=\"2\">Start</td></tr><tr><td>55\"0</td><td/><td colspan=\"2\">Begin to do</td></tr><tr><td>55.1</td><td/><td colspan=\"3\">Be just goingto do</td></tr><tr><td>56</td><td/><td colspan=\"2\">Completion</td></tr><tr><td>56.0</td><td/><td colspan=\"3\">Have finished</td></tr><tr><td>56.1</td><td colspan=\"4\">Do from the begin-ning to the ena</td></tr><tr><td>56.2</td><td/><td colspan=\"3\">Have completed</td></tr><tr><td>57</td><td/><td colspan=\"2\">Limit</td></tr><tr><td>57\"0</td><td/><td colspan=\"3\">Do until the limit</td></tr><tr><td>57\"1</td><td/><td colspan=\"3\">Do throughly</td></tr><tr><td>58</td><td/><td colspan=\"2\">Others</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "Morpheme representing derivative"
            },
            "TABREF9": {
                "html": null,
                "content": "<table><tr><td colspan=\"2\">Class VS ( Vb Vs</td><td/><td>Distribution 1,209 529</td></tr><tr><td/><td/><td/><td>901</td></tr><tr><td>Vg</td><td>Vc</td><td>VB</td><td>951</td></tr><tr><td/><td>VD</td><td/><td>665</td></tr><tr><td>VU 7</td><td>\u00a5S +</td><td>~</td><td>4,255</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "Distribution of matter concepts"
            },
            "TABREF10": {
                "html": null,
                "content": "<table><tr><td>2) TORI [ 1 ] GA SUSUMU.</td></tr><tr><td>THE BIRD[l] MOVES ON.</td></tr><tr><td>3) TORI[2] GA TOBU.</td></tr><tr><td>THE BIRD[l] FLIES.</td></tr><tr><td>4 ) TORI[ 1 ] 6) TORI[1] GAfI NI NORU[2].</td></tr></table>",
                "type_str": "table",
                "num": null,
                "text": "THE BIRD[l] SHIFTS. GA KI NI FURERU. THE BIRD[l] TOUCHES THE TREE. 5 ) TORI [ 1 ] GA KI NI TSUKU. THE BIRD[l] STCKS TO THE TREE. THE BIRD[l] GETS[2] ON THE TREE."
            }
        }
    }
}