Ma Zhiyi

Yao Tianshun

Zhan Xuegong

English

Ma, Zhiyi

49260

Zhan, Xuegong

49261

Yao, Tianshun

49262

Waseda University Repository

Language, Information and Computation(PACLIC 11), 1996, 357-362Extracting Topics from Texts Based on SituationsMa Zhiyi,Zhan Xuegong,Yao TianshunDepartment of Computer Science, Northeastern UniversityShenyang, 110006 ChinaAbstractTo understand text, we must relate it with specified situations.This paper, on the basis of such an idea, discusses how the things thata text describes and the situation that the text relates to are expressedin a computer and how the topic of a text is extracted.1.IntroductionThe topic of a piece of writing is the central idea of it. People not only carefor what it expresses, but also care for the most important idea of it. Therefore,if each piece of writing has a marked topic, then retrieval rate and retrievalprecision of writings can be increased to a great extent. In computers, a piece ofwriting is a text. Only when a text relates to certain situations, can its topic berightly extracted. On the basis of situations matching, the paper presents themethod of extracting topics from texts.A situation is something's world state in a specified moment andenvironment. So, when a thing's attributes are described, the thing's situationsmust be referred to, and at every moment, reasoning only can be executed in asituation. This is why situations are built.A situation is a function of time and space. For example, things and theirbehaviors can constitute different situations at the same time and in differencespace, or in the same space and at difference time by increasing or decreasinga little information.The situation knowledge of something is the sum of perceptual experienceand rational knowledge about a topic. The degree of people understanding a textoften relies on their knowledge level of the field to which the text refers. That is,same or similar situation knowledge is the foundation of people's understandingeach other.Situations may be divided into broad sense situations and narrowsense situations. A broad sense situation includes all dynamic stateevents and static state events in the objective world. It is consecutive tableaus inconsecutive time and space. A narrow sense situation is dynamic state orstatic state events that relates to a specified discourse, that is, it includes theenvironments in which the discourse happens and the events to which thediscourse relates.The task of situation theory is extracting common inner structure from largeamounts of real situations in the objective world, probing into the restrainedrelations among situations, revealing meaning of the language expressions, andpresenting a calculable mathematical model for natural language understandingbased on situations(Sun Bo 1992). A real situation is a set of specific dynamic orstatic events. An abstract situation is a set that isconstructed by some elements357with common inner structure extracted from large amounts of real situations,and it is a mathematical abstraction. Objects, relations among objects, time andspace are all basic elements that establish an abstract situation, and objectsand relations among objects are of recurrent in time and space.2.Text Understanding Based on SituationsNatural language understanding is mainly a process of the close onanalyzing of a text information that reflects its natural features and attributes andjudging of narrated facts, events and behaviors.A text can be understood under a specific situation. The analysis ofnatural language based on situations is as follows:Let r n be an n-gram relation and p be a space-time location. Wedefine < r n,x1,x2,...xn, p > as an event state.An event procedure is a sequence of event states which conforms to acertain time order.An event class is an event procedure that contains undecided elements..For an event class E, if a function f can determine the values of undecidedelements of E, then f. is called a fixed function of E.If E[f] is a subprocedure of an event procedure e, it is called an event of E.If there exists an event procedure e that makes all the variables unique,then e is a context of E.A situation pattern is a causal set of event classes, and its organizationalform is a network-like structure, which uses event classes as its basic nodes.Let E... )] be a causal linkage set, A be space-time variables, Xi beobject variables, where i E 11,4 The following are some definitions:<situation pattern>::= E<event class>,...,<event class>11<event class>::=< r n,X1,...,Xn, >I[< r n,X1,...,Xn, >IAn event state relates to time and space. Now, we analyze the wordswhich express time or space.Location words include region names, orgaization names andcompound location words. The first two can immediately be extracted from atext, but the last can not, it only conjoins the first two and can give a real spacemeaning.Time words may express moment or temporal intervals.Direction words can not be used alone and must be used together withlocation words and time words.Depicting an object still involves concepts, and concepts' meaningdepends on situations. In another word, to express an object, those conceptualattributes, situations that a concept depends on and relations among conceptsmust be depicted. A conceptual structure is described as follows:<conceptual class>::<conceptual name><name of member>::<content of member><member operation mode>::<member operation depicting>•<name of the situation depended>where,• •358<conceptual class> may be general concepts that describecommon attributes of concepts with the same attributes, operative functions,and situations depended on. It may also be special concepts.<name of member> describes names of individual attributes and namesof individual features.<content of member> describes the value of members or restrictions tothe value. It may also be another concept.<member's operation model>::<member's operation depicting> describesoperations that relate to the members in the form of rules.A part of situation structure on concepts is as follows:•••EntryModifier semanticsPragmatic constraint•••When reasoning, the conceptual organization not only restricts the range ofthe searching space, but also benefits reasoning and policy-making undercircumstances that information is of uncertainty or insufficiency.Conceptual situation knowledge is stored in the system collocationdictionary.Sentences in a text describe relations among objects. According to thenature of a sentence that expresses information, sentences are divided intonarrative sentences, descriptive sentences and determining sentences.Narrative sentences express dynamic information. Its situation meaning isan event state or an event process.Descriptive sentences express static information. Its situation meaning ismodifying semantics of a real frameworks.Determine sentences' predicates are copulatives. Determine sentencesare similar to descriptive sentence.Given a text, we combine situations, utilize the intertransmittallanguage of our system CETRAN(Yiao Tianshun 1995),constitute the eventchains, object chains and time chains, establish situation patterns andunderstand it.3.Extracting topicsThe process of extracting the topic from a text based on situations isthat the situation of the text matches the situation patterns that exist in thesystem. The situation pattern is a model of knowledge representation. Inaccordance with a thing happening and developing, the thing's event processesconstitute an event network. If a text's event network can match a situationpattern, the situation that the text describes is a concrete instance of thesituation pattern. Therefore, the system can ascertain that the text's topic isconsistent with the situation pattern's topic.In practice, there is little fully matching between situations. What thepaper discusses is similarities between situations.3.1 Similar RelationsIf there are some similarities between a new situation and the situation359pattern that has existed, then other similarities can be inferred, and somenatures can be processed by the new situation. This is a reasoning process.The process pays attention to similarities between situations, and mayreason under the circumstances that information is of uncertainty orinsufficiency, and can get new information.The process that determines whether two situations are similar can bedivided into two steps:Stepl. The system judges whether the elements which composite asituation are parallelism in the situations' structure, i.e., whether two eventprocesses are similar in the situations' topology.Step2. An event process is an event chain, and each node of the chain is anevent state. If each pair of corresponding event states in two event chains issimilar, then the two situations are similar.Because applied fields are extensive, the paper can not present a generaloutline about the causality among event processes. However, it is veryclear that event states are basic units of a situation network.A knowledge composition of similar reasoning between event states is asfollows:a. set of objects	 obj-setset of relations among objects rela-setset of event states	 es-setb. similar relations among objectssimilar relations among the relationsCompositions of similar relations among event states are as follows:a. corresponding relations among structures of elements which compositeevent states.b. the similarities among elements.The elements include objects and relations among the objects whichcomposite event states.If two event states sl and s2 are similar, then we write sl N s2.sl s2 holds, if and only if there is an isomorphism w between sl and s2.a. Given an object a, if a E obj-set, then w(a) E obj-set, and a N w(a).b. Given a relation r, if r E rela-set, then w(r) E rela-set and r N w(r).c. If sl E es-set, then w(s1) E es-set andw(r(al ,...,an))= (r)(w(al ,...,an))w(	 )= ftw(s1),...,w(snnGiven two element el and e2, if el is a part -of e2, then e2 includesel.Similar relations among objects are symmetric and reflexive, so arethe similar relations among relations and similar relations among event states.Inclusion relations are reflexive and transitive.3.2 Calculating similarity between situationsThe similarity among situations is a measure of relation strength. It may bemeasured in two respects:One is qualitative analysis, and it reflects specific relations' sudden change.The other is quantitative analysis, and it describes specific relations' gradualchange. For example, attributes' values may be compared with one another.360Given two event states, it is not possible that isomorphism among theirelements and difference among their attributes are accurately calculated, onlysimilarity can be calculated. The following is an algorithm that calculatesdegrees of similarity between event states.Given a set S, it includes n objects whose structures are the same, andeach has m attributes. These constitute a relation table O(n,m).Definition 1 Let u be a new object, if its semantic attributes and value fieldsentirely correspond to those in 0, then u and t(t belong to S) have similarity,denoted So, So E [0,1].Definition 2 Let a value field of an attribute Xi be A, if u.Xi E A and t.Xi EA, then u.Xi and t.Xi have similarity, denoted Sa, Sa E [0,1]. So is a function ofSa.Objects that have existed are divided into K groups. The number ofmembers in each group is N1,N2,...,Nk, respectively. Where,kE Ni=ni=1Supposed an attribute X has I difference values x1, x2,...,x1, then the frequencythat each happens is Gx1,Gx2,...,Gxl, Nixj is the number of the group whosevalue is Xj in the ith group. where,kE Nixj=Gxj	 E Nixj=Nxii=1	 j=1Supposed an attribute X happens, and an attribute Y may happen in thesame group, then the probability is denoted Sa(X,Y)=P(YIX).Supposed an attribute X happens in the ith group only, then theprobability of the event P(iIX)=Nix/Gx.Supposed an attribute Y happens in the ith group, then the probability ofthe event P(Yli)=Niy/Ni.Supposed X and Y are independent of each other, and X happens in the ithgroup, and Y may happen in the ith group, then the probability of the eventp(Y1X,O=P(iIX)*P(Yli)=NixNij/GxNi.k	 k NkxjNkxi	 1	 k NkxjNkxiSa(Xi,Xj)= E P(Xj(Xi,K)= E 	 	 E 	K=1	 K=1 GxiNk	 Gxi K=1	 NkIn practice, there are some different focal points. If the focal point is anentire match between the values of attributes, then the similarity of attributes Xand Y is Sa(X,Y)Ib, where b is an integer and b>1.Given two objects u and v, then the similarity between them is .S0(u,v)=(W1*Sa(u.X1,v.X1)+W2*Sa(u.X2,v.X2)+...+Wk*Sa(u.Xk,v.xk))/(w1 +W2+...+Wk)Where, Wi expresses importance degrees of attributes, and its value relates topractical applications. In our system, it is defined as Wi=1-(the number of u.Aiin the training set)/(the number of objects in training set)Structures of the objects that the algorithm processes are the same. Forthe calculation of the similarity of objects whose structures are similar, a fuzzynumber shall be introduced. The idea of its algorithm is similar as above.The relations among objects are composed of expended case361relations, event relations and semantic relations and so on. The whole relationset includes 49 kinds of relations(Yiao Tianshun 1995). The similaritycomparison between relations among objects is a kind of inclusion relation.Calculating similarity between event states is based on results ofcomparison between objects and between relations among objects. Hence, it iseasy to calculate similarity between event states in concrete application fields.4. Conclusion and Future WorkUnderstanding a text, extracting information from it, building a situation,comparing it with other situations, deciding its topic are our works that we havebeen engaging in these years. We have gotten some satisfactory achievements.A text must be understood in a specific situation. The paper discusses howsituation theory is applied in understanding texts, describes the method ofextracting topics from texts and presents an effective algorithm that maycalculate the similarity between event states. We are ready for further researchon causal relations among event processes and the readjustment ofcorresponding parameters in the process of calculating similarity.ReferencesYiao Tianshun.1995.Natural Language Understanding.Tsinghua University.Sun Bo, He Kekang. 1992. A Model Chinese Understanding Based on theSituation Theory. Computer Search and Devolpment.1992(4).Gerard Salton. 1994. Automatic Analysis.	 Theme Generation, andSummarization of Machine-Readable Texts.Science, 264,3.Luo yulong, Li Puo, Zhao qinping. 1995. An Analogical KnowledgeRepresentation and its Logical Description. CA195 China.Han Ke, Li Deyi. 1995. A Resemblance Evaluation Based on StatisticalKnowledge, LDC111A95. Tsinghua University Press.Tang Hongying,Yao Tianshun. 1995. the Chinese Full-Text Analysis Basedon a Sematic Network Computing Algorithm.PNLPPRS'95.362

Extracting Topics from Texts Based on Situations

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Extracting Topics from Texts Based on Situations

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