Lee Chin-Nan

Wang Hsien-Chang

Wang Jhing-Fa

English

DSpace at Waseda University

Domain Unconstrained Language UnderstandingBased on How-net*4. Jhing-Fa Wang, * Hsien-Chang Wang and * Chin-Nan LeeDepartment of Computer Science and Information Engineering+ Department of Electrical EngineeringNational Cheng-Kung University, Taiwan, R.O.C.AbstractIn this paper, we propose a method for domain unconstrained language understandingbased on the How-net knowledge base. The goal is to construct a system that reads in anarticle and answers some related questions. For each sentence in the article, wordsegmentation is first applied. Then, the major components such as agent, theme, event, time,and place are extracted to construct a semantic-slot table and a semantic network. Answers ofthe questions are derived using two approaches, which are based on the relational andhierarchical relation among the major components.Our method is applied to the understanding of the primary-school textbook, and it is able toanswer questions in the exercise of the textbook.1. IntroductionCurrently, most available applications of natural language processing (NLP) are domainspecific. In this paper, we propose a method for domain unconstrained languageunderstanding. The knowledge base we used is the How-net knowledge base, which isconstructed by researchers in Beijing. The description and content of How-net can be found inthe following URL, http://www.how-net.com. How-net describes the relationship of objectsusing both concepts and attributes. Based on How-net knowledge base, we have somemethods to analyze the sentences of an article. First, word segmentation is performed to findthe corresponding word sequence. Then, major components (agent, event, time, place, andtheme) conveyed in the sentence are extracted. Finally, the semantic table and semanticnetwork are constructed for the understanding of the article.For the experiment, we try to answer the questions in the exercises of the primary-schooltextbook. Article of each lesson is used to construct the corresponding semantic table andsemantic network. Answers of the questions are derived by measuring the likelihood of themajor components.This paper is organized as follows. In Section 2, we introduce basic concept of the How-netknowledge base. In Section 3, the method to analyze sentences is described. In Section 4, weshow how to answer the questions of the primary-school textbook. The conclusion is given inthe final Section.2. The Structure of How-netHow-net is a Chinese knowledge base which describes the objects using concepts andattributes. The basic units in the How-net are physical and spiritual objects such ascomponents, attributes, time, space, events, attribute values, and so on.Common and individual characteristics of concepts are both recorded in How-net. Considerthe words "g-(doctor)" and "2ff(patient)" as example, the common characteristic of themis "people". This common characteristic is recorded in the How-net. On the other hand,"doctor" has the individual characteristic being the agent of "cure"; and "patient" has theindividual characteristic being the experiencer of "suffer".The relations among different concepts and attributes are also described by How-net. Therelations are shown below:(1) Upper-Down relation. 	 Ex. Father-Son, Father-Daughter, ..(2) Synonymous relation.	 Ex. Good-Well, Big-Large, ...(3) Antonymous relation.	 Ex. Good-Bad, Large-Small, ...(4) Attribute-Host relation.	 Ex. Age-Person, Color-Flower, ..(5) Component-Entire relation.	 Ex. Leg-Body, Door-House, ..(6) Material-Product relation. 	 Ex. Rice-Wine, Sand-Glass, ...(7) Event-Agent relation.	 Ex. Cure-Doctor, Build-Worker, ..Figure 1 is an example showing relations among some objects (in rectangle shape) andactions (in round-rectangle shape).Figure 1. Example of relations among objects.3. Analysis of the Sentences3.1 Word SegmentationUnlike western language such as English, Chinese sentence is composed by characters.Since How-net is a word-based knowledge base, each sentence has to be segmented intoword-sequence for further processing. Word segmentation can be done by several approaches.The simplest one is to use the greedy algorithm. This algorithm treats the input sentence as alarge string, and then shrinks the string gradually to check whether the shrunk substring is alexical term. The segmentation results may be different if the shrinking directions aredifferent. For example, the sentence "7t_gp.A-istRibt,t4 (The life in college is very cheerful)"may be segmented into " 7t-A-(college student) ?S(live) avery) ilk,(cheerful)" if thestring is shrunk from right to left. However, it may also be segmented into " A•(college) 	(life) j,R(very) 'W(cheerful)", which is the correct segmentation, if shrunk from left toright.In this paper, we employ the greedy algorithm for both shrinking directions. If thesegmentation results are the same for both directions, then we are done. If not, the bigramscores of both word-sequences are calculated to determine the correct segmentation. Thisbigram information is trained using a large amount of news corpus. Since the bigram scorecalculation can be found in many lectures, it is abbreviated here.Note that if the segmentation result contains dangling single-characters, thesesingle-characters may be further combined with other words/characters to form a compoundword using word-formation rules. For example, one of the rules is to deal with the Chinesenaming principle. We try to combine the characters sequence " 	  	  Rt into one word "(Lee Kin-Nan)" since " 	  (Lee)" is a Chinese surname. Another example is that theword sequence 11 •0-2- (thy)	 M(neat)" is combined into the compound wordiP?P (clean)" since it satisfy the adjective formation rule.3.2 Extracting Major Components of the SentenceBased on our previous study [2], we think that to understand a sentence is to know theanswers of SW, i.e., who, what, when, where, and which. In this paper, we define theseanswers as the major components of sentence. The major components represent the agent,theme, time, place, and event conveyed in the sentence and they contain essential informationto understand the sentence.For example, to analyze the sentence "..71 p-fettiiajp (Lee Kin-Nan broke thewindow of the classroom)", the sentence is first segmented into the word sequence: "	 	(Lee Kin-Nan) P(broke) ttV(classroom) 0(De) j (window)". Then, the majorcomponents are determined by the part-of-speech tags found in How-net. In this example, wehave the words: Lee Kin-Nan (agent: n. name of people); break (event: v. break); classroom(place: n. place to conduct a lesson); window (theme: n. the hole in the wall to illuminate thehouse).The major components in such a simple sentence can be easily determined. However, forsome complex sentences, we may need extra information to properly catch the meaning of thesentences. So, the attributes of each component are attached to carry more information. Eachcomponent may have none or more than one attribute. The attribute of agent, for example, canbe the height, weight, age, color, and so on.After extracting the major components of each sentence, the semantic table that consists ofthe major components and their attributes can be constructed sentence by sentence. Figure 2shows an example text chosen from the primary-school textbook. The corresponding semantictable is shown in Table 1. Note that the attribute of an event can be another event or sentence.MMV-_LT7	 °RifriMT*T- ,	 °KM_EziAllqq7A °	 tzfrP_NA ° T*W A	MVRA'-'1'n -W-,",-;11MWTR r TVAin fiTZ afirki gla ' 4, 2a: ° '4 14MAT fEfiUM °MM,FiR r 4 R--VE/1.1.(S i ) My brother is going to school, (5 2) He is very happy.(S3) He is in the first grade, (5 4) I am in the second grade.(S 5) Mother says to me, (S 6) "today is the first day for your brother going to school, (5 7) youshould take him to school carefully.(S8)I go to school with my brother hand in hand.(S9)There are many cars in the road: (S i o) big cars and small cars.(S 11 ) The cars run far and near, (5 12) my brother is afraid of the running cars.(S 13) I told my brother, (5 14) "Don't be afraid. (S 15) Look, the red light is on, (S 16) don't go.(5 17) Wait until the green light is on, (S 18) then cross the road."(5 19) My brother says, (5 20) "OK, I will remember that." Figure 2. The article chosen from the first grade textbook.3.3 Constructing the Semantic NetworkIn addition to the semantic table, semantic network [3,4] also records the relationship ofeach sentence. The nodes of the semantic network are the major components and theirattributes of the sentences. The edges are the relations of major components and attributes.The semantic network is constructed gradually while reading each sentence of the text. Whenthe major components and attributes are extracted, if the component already exists in thesemantic network, the attributes and relations are updated using current ones. Otherwise, newnodes and edges are added into the semantic network. Figure 3 – Figure 6 illustrate theconstruction of semantic network when input the sentences Sl–S4 in Figure 2. Note that newnodes and edges of the semantic network are painted in white color.H4 MItimeN(featureC_EWschoolgo toyoungerbrotherFigure 3. Semantic network construction, step 1:Input the sentence "firZ1-:4.`7*(My brother is going to school)"Figure 4. Semantic network construction, step 2:Input the sentence "faaiff./(He is very happy)"Figure 5. Semantic network construction, step 3:Input the sentence "(My brother is a new student in first grade)Figure 6. Semantic network construction, step 4:Input the sentence "R_E_-:_g-r (I am in second grade)"4. Answering the questionsThe questions we deal with are those in the exercises of primary-school textbook. Eachquestion in the exercise provides several candidate answers for the reader to choose the rightone. Some examples of the questions are shown below.	Ql : It4.11_3- CUT /	 	The classroom is the place to (study / ride).Q2: ( gM. / Ma)	 °If the (red / green) light is on, you can cross the road.Q3: AWIAT: iwin Tivn).Brave is to be (afraid / not afraid).Q4: mm.2:—vAni,i	 /My brother is a new (student / classroom) in the first grade.Q5:There are many (students / cars) in the school.I am in the (first / second) grade.To answer the questions, we have developed two methods. The first one tries to find answerdirectly from the input text. The other one calculates the relation score among majorcomponents extracted from the questions and the article to find the proper answer.4.1 Answering the Question Directly from the TextSome questions of the primary-school textbook are easy to answer. The answer can befound by scanning the article to match the sentence that has the same components in question.For example, Q6 is a question of this kind. To answer question Q6, we first extract thecomponents of the question, i.e., "R(I)". This component is used to active the correspondingsubgraph in the semantic network. Then, the answers "—g(first grade), iiiiR(secondgrade)" are determined by choosing the one which matches the components best. In this case,the answer will be "ii it(second grade)".4.2 Answering the Question According to the Similarity MeasureIf the method described in Section 4.1 does not work, we use another approach to find theright answer. This approach checks the similarities of the major components in the question.The similarities are measured by the relation derived from the How-net. Two kinds ofsimilarities, i.e., relational and hierarchical similarity, are used to calculate the overallsimilarity measure.4.2.1	 Measuring Relational SimilarityIn the How-net knowledge base, each word has its corresponding definition(s). Thesedefinitions are recorded in How-net as the field "DEF". For example, the words "fflt-"(school)", "(student))", and " nrnicar) have the DEFs as shown below. Note that a wordmay have several meanings, thus it has many DEFs.W_C[49479]=VSZ II word indexG_C = N //part of speech tagDEF[0]=InstitutePlaceligffi // definition of the word's conceptFeature of noun: *engagelft#affairsl.AFeature of verb: engagelfX${agent,content}DEF[1]=©teachigFeature of verb: teachig{agent,content,target}/{agent,patient,ResultEvent}DEF[2]=@studyl,Feature of verb: studyiNagent,content,source}DEF[3]=educationigWFeature of noun : educationlgtW_C[49451]=TG C = NDEF[0]=humanIAFeature of noun: N.1.1.1.1.1.1!namelt!wisdomitig!abilitylgjj!occupationlffafractiffMDEF[1]=*studffFeature of verb : studyffiagent,content,source}DEF[2]=educationigftFeature of noun :education 	W_C[34290]=AG_C = NDEF[0]=LandVehiclelFeature of noun: N.1.1.1.2.2.7.3.1#1andil*WeVehicleGoIRFeature of verb: VehicleGoll.{agent,direction,Locationlni,LocationFin}The similarity of different words is measured by comparing their DEFs. Words with similarDEFs will result in higher similarity measure. For example, the relational similarity of thewords " ffltk(school)" , " _W_(student))" , and " n(car)" are calculated as:N .1 .1 .2mental3kN.1 .1 .3event4r -ft-N.1entity-or intN.2attributeN.3quantityAtt_lk N.1.1.1.1animate	 ethiN.1.1.1.1.1AnimalHuman *Ji4hJN.1.1.1.1.2plant tk 4-b7N.1.1.1.1.3bacterial	 4-h3N.1.1.1.2.1nature jc 5 4bJN.1.1.1.2.2artifact .1.. s._ 4b,N.1.1.1.1.1.1human ,nC_N.1.1.1.1.1.2animal lifkN.1.1.1phsicalfi 'WN.1.1.1.2inanimate -"-±_ 01-N.1.1.1.3shape N.1.1.2.1 emotion '1*,.6.N.1.1.2.2 expr ienceN.1.1.2.3 aspiration ;tbitikN.1.1.2.4 thinkingN.1.1.2.5 informationN.1.1.2.6 regulation #4?-4E.N.1 .1 .2.7 right 441 +.1N.1.1.2.8 dutyN.1.1.3.1 fact ir 4*-N.1.1.3.2 phenomena	 St.N.1.1.2.4.1 thoughN.1.1.2.4.2 method 97-51	  N.1.1.2.4.3 purpose ill 6,3N.1.1.2.4.4 reason 1.1_- N.1.1.2.4.5 standpoint- N.1.1.2.4.6 knowledge	 ,=7SilizN.1.1.3.2.1 disease 4X•N.1.1- thing4b1N.1.2	 N.1.1.4.1 institution 44,k44*time.4- FA	 N.1.1.4	 N.1.1.4.2 army	-component - N.1.1.4.3 InstitutePlace **/.1--4rsN.1.1.4.4 community Mil ftN.1.3	 N.1.3.1 direction 75-- f7space -N.1.3.2 location 4L 1N.1.4	 N.1.4.1 part is ft-componentN_1.4.2 fitting me. ft-Relational Similarity (No.49479	& No.49451 Vt') =12Relational Similarity (No.49479 	 	 & No.34290 	= 2Relational Similarity (No.49451 ''fit' & No.34290	 = 3From above result, we can conclude that the word "VOschool)" is more similar to(student)" than "I-1,  ift(car)".4.2.2	 Measuring Hierarchical SimilarityHow-net also specifies the hierarchical relation of entities like a tree structure as shownin Figure 7. In this paper, the hierarchical similarity of two objects is defined as theshortest-path distance between these objects. For example, the similarity measure of "human"and "animal" is two, as shown is the upper-right part of Figure 7.Figure 7. The hierarchical relation of objects.—334--4.2.3	 Answering the Question According to the Similarity MeasureWith the similarity measures obtained by the above methods (relational and hierarchical),we are able to answer questions that can not be answered using the method in Section 4. Forexample, the similarity measure of major components in Q4 and Q5 can be calculated asbelow.Q4: '/I_'---1T-VAFYJ	 /	 °My brother is a (new student / classroom) in the first grade.Major components: "(brother)", 111(new student)", "(classroom)"Similarity measure (No.10017'W & No.48295W 1 ) = 10Similarity measure (No.100171 MW & No.221917A1 = 0The answer is : "Vit(new student)"Q5: Tf3Z1I-4M-in	 /	 °There are many (students / cars) in the school.Major components: "PM(school)","3t.(student)",",*(car)"Relational Similarity (No.49479 	 	 & No.49451	 = 12Relational Similarity (No.49479 'VSz' & No.34290	 = 2The answer is : "04.44.(student)"5. Discussion and ConclusionAs the technique of speech processing improved, the success of speech applications,such as human-machine interactive system and spoken dialogue system, now depends on thesuccess of natural language processing. Currently, few significant results are presented forChinese NLP due to the lack of good knowledge base. In this paper, we employ the How-netto be our knowledge base for the domain unconstrained language understanding. Methods toanswer the questions of the input article are proposed. Word segmentation, major componentextracting, and semantic network construction of the input article enable us to derive theanswer of the questions in the primary-school textbook.Although the How-net plays an important role in our system, we find that some adaptationsshould be made to achieve better performance for the Chinese language understanding inTaiwan. The major shortcoming is that How-net is built in the Mainland China, thus somewording habits are different from that in Taiwan. For instance, the word "software" means "Vx" in Taiwan, however, it is written as " 	 " in Mainland China. This kind ofword-disagreement should be solved before the How-net can be applied deeply in the naturallanguage processing of Chinese in Taiwan. We are currently enhancing the How-net by usingthe word dictionary built by the Academic Sinica of Taiwan [5].Our future research is to apply the techniques described in this paper to the task of webunderstanding.6. References[1] Zhen-Dong Dong, The How-net web site, http://www.how-net.com/[2] Thing-Fa Wang, Hsien-Chang Wang, Chin-Nan Lee and Mao-Sheng Hung, "On theConstruction of the Knowledge Base for the Domain Unconstrained Spoken DialogueSystem", Oriental COCOSDA 1999, pp.133-136.[3] J. Allen, Natural Language Understanding, Second Edition, The Benjamin/CummingsPublishing Company, Inc. 1995.[4] J. Rumbaugh, et. al. Object-Oriented Modeling and Design, Prentice Hall, 1991.[5] FrI Z-Mignii\M" 141 3ZMW}C (EN)", 41 fiic-fARME1Af454=F3, 1993.o tochoolMEManyig andsmallItd, onreen, 0RSentence classificationDeclarativeDeclarativeDeclarativeperiodTimepoint attrib. attrib.S152S3S4	 DeclarativeS5	 SubordinationS6	 DeclarativeSll	 DeclarativeS12	 DeclarativeS14	 ImperativeS15	 DeclarativeS16	 Imperative517	 ImperativeS18	 Adverbial clauseS19	 DeclarativeS20	 AffirmativeAgent	 attribute EventTrans. Intrans. attribute Theme attributePlaceto fromS7	 Imperative58	 DeclarativeS9	 DeclarativeS10	 DeclarativeS13	 Subordination(period)Today. (at) School(at) RoadRoadTable 1. The semantic-slot table for the text in Figure 2-338-

Domain Unconstrained Language Understanding Based on How-net

Wang, Jhing-Fa

49512

Wang, Hsien-Chang

49513

Lee, Chin-Nan

49514

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Domain Unconstrained Language Understanding Based on How-net

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