{ "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_ :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(hSym-bolic data associated with visualfeatures. Some of them correspond toChomsky's syntactic features in thelexicon. 6Concept dataData obtained by or-ganizing conceptual features. Mostdata have names as words. In case ofthe verb they roughly correspond toMinsky,s surface semantic frames. 7Interconnected concept dataNet-works of concept data. A concept canbe interconnected with other con-cepts from various viewpoints.Interconnectedconcept data[Level 4[Data of conceptlLevel 3Data of concep-]tual featuresLevel 2Data of visualI features003 Level 1H~Raw data(Level 0 ')]~ ~ Lktion process]", "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": "
No. Pattern
I
Z
IV
", "type_str": "table", "num": null, "text": "" }, "TABREF3": { "html": null, "content": "
of semantic contents
", "type_str": "table", "num": null, "text": "" }, "TABREF4": { "html": null, "content": "
watasu*
(pass)
yuzuru \u1e7d (hand overx
ataerx'x
(give)o/uruu okurukasuazukeru
(sell)(present)(lend)(deposit)
orosu* Simple matter
(sell by wholesale)concept
", "type_str": "table", "num": null, "text": "and Table 4." }, "TABREF5": { "html": null, "content": "
Connecting rules of complex concept A
ConnectingExampleRemark
rule
XXIcause and
effect
XXI.IImplication(mizu-ga) afure-Jeru.If water overflows,
(Water) overflow-comes out.water comes out.
XXDKCause and effect(dareka-ga watashi -o) oshiltaosu.If someone ~ushes me,
(Some one) push-throws (me) down.am thrown down.
XXKLogical product(sinja-ga) fushi-ogamu.Believers kneel down
(Believers) kneel down-pray.and pray.
XX]I[Syntactic
connection
XX ]]I.lRelation between(akago-ga) naki-yamu.That a baby cries stops
s and v
XX]I[']IRelation between(anauns~-ga genko-o) yomi-ayamaru.An anouncer misses to
o and v(An anouncer) read-misses (his manus-cript).read his manuscript.
XX]II.IKRelation between o w and v(kanshu-~a sh~jin-o) tatakz-okosu. (A ~uard) knock-awakes (prisoners).A guard awakes \u2022 risoners y knockin~ tNem.
", "type_str": "table", "num": null, "text": "No." }, "TABREF6": { "html": null, "content": "
concept
yuzuru(hand over)
ataeru(give)
~u(sell)
orosu(sell by
wholesale)
oku~(present)
Vx
kasu (lend)
", "type_str": "table", "num": null, "text": "" }, "TABREF7": { "html": null, "content": "
No.ContentsExampleDis
i0Spiritual act
i0.0Thought.recognitionmitomeru(recog-35
nize)
i0 .iGuess.judgementsassuru(guess)25
i0.2Respect.contemptuyamau(respect)18
i0.3Haughty.flatteryhikerakasu(sport~20
11Academic and artistic
act
ii \"0Education.learningoshieru(teach)33
ii \"ICreationarawasu(write aii
book)
12Religious act
12 \"0Beliefm$deru (visit a16
temple or shirine
12 .iCelebration.marriage.totsugu (marry)16
funeral
13Verbal act
13 \"0Praise.blamehomeru(praise)12
13 .iInstigation.banteriodateru(insti-12
igate)
14Social act
14 \"0Lifei kurasu (live)26
14 .iFosteringyashinau (bring up)26
14.2Antisocial. immoralnusumu (steal)43
14 \"3Promise.negotiationi suppokasu (breake35
an appointment)
15Conduct.behavioraumasu(assume a25
prim air)
16Labour.production
16.0Labour.worktsutomeru(serve)35
16 .iAgriculture.industry commerceakinau(deal in)49
i7Possesion
17.0Owning.abandonementy~suru(own)ii
17 .iGetting and giving. losingataeru(give)55
17 -2Selling and buying. lending and borrowingkau (buy)19
iSInvestigation.meas-urement
18.0Investigationshiraberu(inves-24
tigate)
18 .iMeasurementhakaru(measure)19
59Domination.personal-affairs
19.0Domination-obediencesuberu(dominate)32
19 .iPersonal affairsyatou(employ)14
2OAttack and defense
victory and defeat
20.0Attack and defensesemeru(attack)26
20 .iVictory and defeat-makasu(defeat)
superiority and infe-19
riority
21Refuge.escapenigeru(escape)22
22Rise and fall.pros-
perity and decline
22.0 Rise and fallhorobosu(ruin)Ii
22.1 Prosperity and de-sakaeru(prosper) 19
cline
23Othersmoyoosu(hold a333
meeting)
TotalI~041
", "type_str": "table", "num": null, "text": "Surface contents of complex concept B" }, "TABREF8": { "html": null, "content": "
operators
No.MorphemeExampleRemark
LAffixkanashi-'garu '' be sad
(sad-\"garu \")
LI! Formative to
conform affix
LI.IPrefixal\"tor~\"chirakasuscatter about
(\"take\"-scatterawfully
about)
LI'IIISuffixalakire-\"kaeru\"be thoroughly
(he amazed-\"re-amazed
turn\")
LI[Others
Table 7Derivative information
No.Derivative informationExample
50Emphasis
50.0Emphasis\"tori\"-chirakasu
~'takdLscatter about)
50.1Do completely
50.2Do violently
Respect.politeness.
humbleness
52Vulgarity
53Poor practice.fai~
ure
53'0Be ill able to do
53\"1Lose a chance to do
53\"2Fail to do in part
53\"3!Fail to do
54Repetition.habit
54\"01Do again
54.1Be used to do
55Start
55\"0Begin to do
55.1Be just goingto do
56Completion
56.0Have finished
56.1Do from the begin-ning to the ena
56.2Have completed
57Limit
57\"0Do until the limit
57\"1Do throughly
58Others
", "type_str": "table", "num": null, "text": "Morpheme representing derivative" }, "TABREF9": { "html": null, "content": "
Class VS ( Vb VsDistribution 1,209 529
901
VgVcVB951
VD665
VU 7\u00a5S +~4,255
", "type_str": "table", "num": null, "text": "Distribution of matter concepts" }, "TABREF10": { "html": null, "content": "
2) TORI [ 1 ] GA SUSUMU.
THE BIRD[l] MOVES ON.
3) TORI[2] GA TOBU.
THE BIRD[l] FLIES.
4 ) TORI[ 1 ] 6) TORI[1] GAfI NI NORU[2].
", "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." } } } }