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<?xml version="1.0" standalone="yes"?> <Paper uid="C88-1065"> <Title>Exploiting Lexical Regularities in Designing Natural Language Systems</Title> <Section position="4" start_page="0" end_page="317" type="metho"> <SectionTitle> MIT Lexicon Project (Rappaport, Levin, and Laughren \[1988\], </SectionTitle> <Paragraph position="0"> Levin \[1985\], Hale and Keyser \[1986\], Levin and Rappaport \[to appear\]). These studies have established a range of semantic-syntactic interdependencies exhibited by semantically coherent classes of verbs and have identified a number of essential classes of verbs, as well as the central properties characterizing verbs of each type.</Paragraph> <Paragraph position="1"> The results of this work have been used in the design of a lexical component for the START natural language system developed at the MIT Artificial Intelligence Laboratory (Katz \[1988\]). In this paper we show how these resttlgs allow STAtt2\[' to attain a broader range of coverage than most existing systems while maintaining modular organization.</Paragraph> <Paragraph position="2"> A verb d,~notes an action, state, or process involving one or more participants, which we refer to as the arguments of the verb. Solae verbs may express their argtunents in more than one way, sometimes wi~h slightly different semantic interpretations. We say th*~t such verbs patriciate in argument alternations. We have seen that certain verbs have both transitive and intransitive uses, and That the relationship between the uses is not uniform across all eerbs; rather, it is a property of the verb involved. We say theft such verbs participate in transitivity alternations, a subclass of argument alternations. So far, we have seen the indefinite object alternation, in (9), and the reflexive alternation, in (10): (9) Sally ~de a pear. :~:,-,~ Sally ate. (i.e., Sally ate some food.) (10) David dressed the baby. ::~ David dressed. (i.e., David dressed himself.) These are only two of about a dozen transitivity alternations found in English. (See Atkins, Keg/, and Levln \[1986\], Hale and Keyser It9~t6\], Levin \[1985\] for a description of various other altern~tion.~l.) A question that a system designer might ask is whether the argument Mternations a verb participates in are predictable or whether they are merely an idiosyncratic property of that w~rt,. This question is clearly also of linguistic interest. Argument alternations, including transitivity alternations, have receiw~d considerable attention from theoretical linguists. It tin'as out that each alternation is associated with particular semantic types of verbs. Thus the verbs participating in the indefinite object alternation ate all activity verbs; most of them describe typical occup.ations: a (11) drink, eat, file, iron, plow, read, sweep, type, write .... The verbs pa~rtieipating in the reflexive alternation are all verbs of grooming: (12) bathe, brnsh, change, comb, dress, shave, shower, wash, ... Studies of phenon:ena such as those described here reveal that Englis:h verbs are organized into classes on the basis of shared components of meaning. The members of these classes share certai:: syntactic properties, specifically properties concerning the possible expressions of their arguments. To state this differently, certain linguistically relewnt aspects of a verb's meaning serve as a pointer to its place in the organizational scheme of English verbs. Once this place is identified, various syntactic properties of a verb can be determined.</Paragraph> <Paragraph position="3"> For linguists working on the lexicon the study of alter- i nations can provide insight into linguistically relevant aspects of meaning due to the interdependency between the syntactic and semantic properties of a verb. (For instance, see Hale and Keyser \[1987\] for a discussion of another transitivity alternation, the middle ttlternation, from this perspective). For eomptttational linguists this characteristic of lexicM organization suggests a modtflar system design: many lexical properties can be associated with verb classes, and need not be registered in the lexical entries of individual verbs, which can simply indicate class rnember~hip. These ideas are incorporated into the design of tb.e START system, as we describe in detail in the remainder of the paper.</Paragraph> <Paragraph position="4"> 1 In certain circumstances, a much wider range of English transitive verbs show an intransitlve use with an indefinite object interpretation: when they are used by people whose activity involves the action denoted by the verb to describe the activity. For example, the verb stuff does not usually occur without an object, ~She stuffs, but if this verb is used to describe the activity of someone Who stuffs pillows in a pillow-factory, then it would be M1 right to use the verb :ntransitively.</Paragraph> <Paragraph position="5"> 3. An Overview of the START system The START natural language system (SynTactic Analysis using Reversible Transformations) consists of two modules which share the same Grammar (see Katz \[1980\], Katz and Winston \[1982\]). The understanding module analyzes English text and produces a knowledge base which incorporates the information found in the text. Given an appropriate segment of the knowledge base, the generating module produces English sentences. A user can retrieve the information stored in the knowledge base by querying it in English. The system will then produce an English response.</Paragraph> <Paragraph position="6"> START has been used by researchers at MIT, Stanford University, and the Jet Propulsion Laboratory for constructing and querying knowledge bases using English. (See, for example, Winston \[1982\], \[1984\], Winston, Binford, Katz, and Lowry \[1983\], Doyle \[1984\], Katz and Brooks \[19871).</Paragraph> <Paragraph position="7"> START rearranges the elements of the parse tree it constructs into embedded ternary expressions (T-expressions) by tying together the three most salient parameters of a sentence, the subject, the object, and the relation between them, <subject relation object>. For instance, the sentence (13) Gabriella might buy some stickers will result in the T-expression <Gabriella buy stickers>.</Paragraph> <Paragraph position="8"> Certain other parameters (adjectives, possessive nouns, prepositional phrases, etc.) are used to create additional T-expressions in which prepositions and several special words serve as rela.finns. null The remaining parameters--adverbs and their position, tense, auxiliaries, voice, negation, etc.--are recorded in a represen. rational structure called history. The history has a page pertaining to each sentence which yields the given T-expression. When we index the T-expression in the knowledge base, we cross-reference its three components and attach the history H to it. One can thus think of the resulting entry in the knowledge base as a &quot;digested summary&quot; of the syntactic structure of English sentences.</Paragraph> <Paragraph position="9"> In order to handle embedded sentences, START allows any T-expression to take another T-expression as its subject or ob-jeer. START can analyze and generate sentences with arb:trarily complex embedded structures.</Paragraph> <Paragraph position="10"> We conclude our description of START with a brief overview of how the system answers questions. Questions are requests for information from START's knowledge base. In order to answer a question START must translate the question into a T-expression template which can be used to search the knowledge base for T-expressions which contain infornmtion relevant to providing an answer to the question. We illustrate the actual question~ answering process with an example. Suppose that as a result of analyzing and indexing a text containing sentence (14), the knowledge base contains T-expression (15): (14) Mary presented Paul with a gift.</Paragraph> <Paragraph position="11"> (15) <<Mary present Paul> with gift> Now suppose that a user asks START the following wh-question: (16) Whom did Mary present with a gift? In the context of.(14) the answer is Paul. In order to determine this, the system must first turn the question into a T-expression template that can be used to search the knowledge base. The first step in this process is to undo the effects of the ~h-movement transformation that is used to create English whquestions. To do this, START must find the place in sentence (16) that the wh-word whom came from and then insert the wh-word in this position: (17) Mary presented whom with a gift.</Paragraph> <Paragraph position="12"> Next the language understanding system leads sentence (17) through the same flow of control as any other declarative sentence and produces the following T-expression which serves as a pattern used to query the knowledge base: (18) <<Mary present whom> with gift> Treating whom as a matching variable, the system feeds T-expression (18) through a matcher in order to determine whether there is anything in the knowledge base that matches (18). The marcher finds the T-expression created from (14): (19) <<Mary present Paul> with gift> and the language generation system then uses this T-expression to produce the English response to question (16): (20) Mary presented Paul with a gift.</Paragraph> <Paragraph position="13"> START handles yes-no questions in a similar fashion. Suppose that START had been asked the yes-no question (21) Did Mary present Paul with a gift? As in the wh-case, START would turn this question into a T-expression template that could be matched against the T-expressions in the knowledge base. The difference between yeano and wh-questions is that the T-expression template generated by a yes-no question would contain no variables. The question above would generate the template <<Mary present Paul> with gift> which would match against (19), allowing the system to answer: (22) Yes, Mary presented Paul with a gift.</Paragraph> </Section> <Section position="5" start_page="317" end_page="318" type="metho"> <SectionTitle> 4. Introducing S-rules </SectionTitle> <Paragraph position="0"> Since T-expressions in the START system are built using the pattern <subject relation object> at every level of embedding, they mimic the hierarchical organization of English sentences. As a consequence, sentences differing in their surface syntax but close in meaning are not considered similar by the system. For example, given sentence (23) as input, START will create an embedded T-expression (24), whereas a near paraphrase, sentence (25), will generate T-expression (26): (23) Mary presented Paul with a gift (24) <<Mary present Paul> with gift> (25) Mary presented a gift to Paul (26) <<Mary present gift> to Paul> Speakers of English know that sentences (23) and (25) both describe a transfer of possession. In both sentences, the gift is the transferred object, Paul is the recipient of this object, and Mary is the agent of the transfer~ despite different syntactic realizations of some of these arguments. It seems natural that this kind of knowledge be available to a natural language system. However, START, as described so far, does not consider T-expressions (24) and (26), which are associated with these sentences, to be similar.</Paragraph> <Paragraph position="1"> The difference in the T-expressions becomes particularly problematic when START is asked a question. An example will clarify this point. Suppose the input text contains only one present sentence, (27), and the knowledge base contMns only the corresponding T-expression, (28): (27) Mary presented Paul with a gift (28) <<Mary present Paul> with gift> Now suppose the user asked the following question: (29) To whom did Mary present a gift? Although a speaker of English could easily answer this question after being told sentence (27), START would not be able to answer it. This question presents a problem for START because T-expression (30) produced by question (29) will not match T-expression (28).</Paragraph> <Paragraph position="2"> (30) <<Mary present gift> to whom> START is unable to answer such questions because it is unaware of the interactions between the syntactic and semantic properties of verbs. This limitation is a serious drawback since interactions similar to the one just described pervade the English language and, therefore, cannot be ignored in the construction of a natural language system.</Paragraph> <Paragraph position="3"> The present example illustrates that START needs information that allows it to deduce the relationship between alternate realizations of the arguments of verbs. In this instance, we want START to know that whenever A presents B with C, then A presents C to B. We do this by introducing rules that make explicit the relationship between alternate realizations of the arguments of verbs. We call such rules S~rules (where S stands for both Syntax and Semantics). Here is the S-rule that solves the problem caused by the verb present:</Paragraph> <Section position="1" start_page="318" end_page="318" type="sub_section"> <SectionTitle> (31) Present S-rule </SectionTitle> <Paragraph position="0"> If <<subject present object1> with object2> Then <<subject present object2> to objectl> S-rules are implemented as a rule-based system. Each S-rule is made up of two parts, an antecedent (the IF-clause) and a consequent (the THEN-clause). Each clause consists of a set of templates for T-expressions, where the template elements are filled by variables or constants. For example, the Present S-rule contains three variables, subject, object1, object2, which are used to represent the noun phrases in the T-expressions. This rule also contains three constants, present, with, and to, shown in boldface. The Present S-rule will apply only to T-expressions which involve the verb present and which meet the additional structural constraints.</Paragraph> <Paragraph position="1"> S-rules operate in two modes: forward and backward. We describe both modes of operation briefly, although in this paper we concentrate on S-rules operating in the backward mode, since this mode is most useful in a Question-Answering natural language system.</Paragraph> <Paragraph position="2"> When triggered by certain conditions, S-rules in the forward mode allow the system to intercept T-expressions produced by the understanding module, transform or augment them in a way specified by the rule, and then incorporate the result into the knowledge base. For instance, if the Present S-rule is used in the forward mode, as soon as its antecedent matches T-expression (32) produced by the understanding module, it creates a new T-expression (33) and then adds it to the knowledge base: (32) <<Mary present Paul> with gift> (33) <<Mary present gift> to Paul> Now question (29) can be answered since T-expression (30) associated with this question matches against T-expression (33). The generating module of START responds: (34) Mary presented a gift to Paul.</Paragraph> <Paragraph position="3"> All additional facts produced by the forward S-rules are in-. stantly entered in the knowledge basel The forward mode is especially useful when the information processed by START is put into action by another computer system because in such a situation START ought to provide the interfacing system with as much data as possible.</Paragraph> <Paragraph position="4"> In contrast, the backward mode is employed when the user queries the knowledge base. Often for reasons of computational e/l~cleney, it is advantageous not to incorporate all inferred knowledge into the knowledge base immediately. S-rules in the bac:kward mode trigger only when a request comes in which cannot be answered directly, initiating a search in the knowledge base to determine if the answer can be deduced from the available information. For example, the Present S-rule used in the backward mode does not trigger when sentence (27) is read and T-expression (28) is produced by START. The S-rule triggers only when question (29) is asked since this question cannot be answered directly.</Paragraph> </Section> </Section> <Section position="6" start_page="318" end_page="519" type="metho"> <SectionTitle> 5. The I,exical Component of START </SectionTitle> <Paragraph position="0"> In order to lmderstand an English sentence, the START system needs to have morphological, syntactic, and semantic information about the words in the sentence. All the words that the system is aware of, along with information about their part of speech, inflection, gender ~, number, etc. are stored in the Lexicon. Virtually every branch of START uses the Lexicon to accomplish i~s task. In this section we discuss the way in which the Lexicon extends the system's ability to dcal with semantic-syntactic interdependencies. We show that the Lexicon provides a place where a verb's membership in a semantic class can be registered, a)lowing more general S-rules to be stated.</Paragraph> <Paragraph position="1"> To exantine how lexieal information about verb classes may be utilized by the S-rules, we introduce another argument alternation in English, the property-factoring alternation (Van Oosten \[1980\]). Consider the following sentence: (35) Paul surprised the audience with his answer.</Paragraph> <Paragraph position="2"> An English speaker knows that sentence (35) can be paraphrased (36) Paul's answer surprised the audience.</Paragraph> <Paragraph position="3"> Notice that in (35), the subject brings about the emotional reaction (surprise) by means of some property expressed in the with phrase. Sentence (36) describes the same emotionM reaction as in (35) but in (36) the property and its possessor are collapsed into a single noun phrase.</Paragraph> <Paragraph position="4"> Suppose that after sentence (35) is typed into the computer, we ask: (37) Did Paul's answer surprise the audience? While a speaker of English would know that the answer to this question is Yes, this reply is not obvious to START since T-expressions :related to sentence (35) and question (37) are very different: 2 (38) <<Paul surprise audience> with answer> (39) <answer surprise audience> Extending t:he approach taken to the example with the verb present in section 4, we could formulate a simple S-rule that could be used to answer question (37). The Surprise S-rule (40), like the Present S-rule, makes explicit the relationship between the alternate realizations of the arguments of the verb surprise: '2'1'o simplify the exposition we do not show the T-expression describing the relation between the property (answer) and its possessor (P,~'.l).</Paragraph> <Paragraph position="5"> (40) Surprise S-rule If <<subject surprise object1> with object2> Then <object2 surprise object1> In taking this approach we are explicitly associating the property-factoring alternation with the verb surprise; we are assuming that it is an idiosyncratic property of the verb.</Paragraph> <Paragraph position="6"> Formulating a special purpose S-rule which applies only to the verb surprise does not seem to be the best solution to the problem. Surprise is only one of many verbs which exhibit the property-factoring alternation. This alternation holds of a large class consisting of over one hundred verbs, among them (41) anger, annoy, embarrass, frighten, impress, please, ... For example: (42) Miriam amused Jessica with her performance.</Paragraph> <Paragraph position="7"> (43) Miriam's performance amused Jessica.</Paragraph> <Paragraph position="8"> (44) Gabriella scared the turtle with a sudden movement. (45) Gabriella's sudden movement scared 'the turtle.</Paragraph> <Paragraph position="9"> These verbs also share a certain semantic property: they all denote emotional reactions. For this reason we identify a class of emotional-reaction verbs and say that the property of the verb surprise responsible for the alternation shown in (35) and (36) holds for all verbs that comprise the emotional-reaction class. 3 Once we have tied the ability to participate in the property-factoring alternation to a particular class of verbs, we no longer need to indicate this property in the lexical entry of each verb in the class or write verb-specific S-rules, such as the Surprise S-rule. Rather, we can associate the alternation with the class and then simply indicate in the lcxical entry of a verb whether it belongs to tlfis class. That is, we augment a verb's lexical entry with an indication of its semantic class membership. For instance, we would register in the entry for surprise that it is a member of the emotional-reaction class. 4 (46) (surprise :verb :emotional-reaction) Now instead of writing a number of verb-specific S-rules, we can write a single general S-rule which triggers not only on the verb surprise, but on any verb fl'om the emotional-reaction class: The revised S-rule contains a PROVIDI~D clause which specifies the class of verbs to which the rule applies, ensuring that it applies to the emotional-reaction verbs.</Paragraph> <Paragraph position="10"> When question (37) is asked, the Property-factoring S-rule (used in the backward mode) will trigger, since the T-expression tic literature because of these and other characteristic properties that set this class apart. (See Postal \[1971\], Pesetsky \[1987\], Belletti and R.izzi \[1986\], Grimshaw \[to appea-\], and many others). This class of verbs which take the experiencer argument (the per-son experiencing the emotions) as object should be distinguished from a second class of verbs of psychological state which take the experlencer argument as subject. The latter class which includes verbs like admire, detest, esteem, hate, and like will not be discussed in this paper.</Paragraph> <Paragraph position="11"> produced by the question matches the THEN-part of the rule, and furthermore, the verb surprise belongs to the emotional-reaction class. The correct answer to question (37) is deduced when the appropriately instantiated IF-part of the S-rule is matched to T-expression (38) found in the knowledge base. Here is how START responds: (49) Yes, Paul's answer surprised the audience.</Paragraph> <Paragraph position="12"> The PROVIDED restriction of S-rule (47) not only allows the rule to apply to verbs of the appropriate semantic type, but it also prevents the rule from applying to verbs that do not display the property-factoring alternation. For instance, the verbs surprise and present can express their arguments in a similar fashion--both are found in the context \[NP V NP with NP\], but they differ in the other realizations of their arg-uments. Specifically present does not participate in the property-factoring alternation, as (50) shows, nor does surprise participate in the alternation that present participates in, as (51) shows: (50) Mary presented Paul with a gift.</Paragraph> <Paragraph position="13"> *Mary's gift presented Paul.</Paragraph> <Paragraph position="14"> (51) Paul surprised the audience With his answer.</Paragraph> <Paragraph position="15"> *Paul surprised his answer to the audience.</Paragraph> <Paragraph position="16"> In the absence of the PROVIDED clause, the Property-factoring S-rule could potentially misapply to verbs like present.</Paragraph> <Paragraph position="17"> The surprise example shows how the addition of information about semantic class membership to verb entries allows the system to handle a particular phenomenon (or lexieal property) common to all verbs in a particular class, with the help of a single S-rule. We refer to this approach as the verb class approach. We could have achieved the same effect in another way: instead of specifying verb class membership in the entry of each verb, we could have explicitly registered the lexical properties that apply to the verb (or the names of the corresponding Srules). Taking this approach, the lexical entry for surprise would indicate property-factoring instead of emotionabreaction but would otherwise be unchanged.</Paragraph> <Paragraph position="18"> (52) (surprise :verb :property-factorlng) This approach could allow us to dispense with the PROVIDED clause in the S-rules since the lexical entry of a verb would be tagged with the set of S-rules that could apply to that verb. However, the verb class approach has a clear advantage over the alternative lexical property approach when more than one property is involved. Typically each semantic class of verbs has a number of properties associated with it, which must ultimately be handled by a natural language system.</Paragraph> <Paragraph position="19"> If we take the lexical property approach, whenever we add a new lexical property, we would need to write an S-rule for this new property, and we would then have to add the property to the lexical entry of each member of the class of verbs that exhibit this property.</Paragraph> <Paragraph position="20"> In contrast, if we take the verb class approach, it is easy to extend the system to handle new properties of a class of verbs. All that is required is the addition of the appropriate S-rule, formulated so that it triggers on this class of verbs. There is no need to alter the lexical entries of the members of the class in any way if the lexical entry of each verb in the class indicates that it is a member of this class. Thus the verb class approach, unlike the lexical property approach, allows a more modular system design; this in turn allows the coverage of the system to be extended more easily.</Paragraph> <Paragraph position="21"> To illustrate that each class of verbs is characterized by a whole cluster of properties, we survey just a few of the properties associated with the emotional-reaction verbs. They participate in several transitivity alternations--the middle alternation in (53) and (54) (see Keyser and Roeper \[1984\], Hale and Keyser \[1987\], among others) and the null-object alternation in (55) and (56) (aizzi \[1986\]): (53) Dogs frighten little children.</Paragraph> <Paragraph position="22"> (54) Little children frighten easily.</Paragraph> <Paragraph position="23"> (55) Thunder never fails to frighten people.</Paragraph> <Paragraph position="24"> (56) Thunder never fails to frighten.</Paragraph> <Paragraph position="25"> The subject of the middle use (54) bears the same semantic relation to the verb as the object of the transitive use (53), while the subject of the nnll-object use (56) bears the same semantic relation to the verb as the subject of the transitive use (55). The null-object alternation, like the indefinite object alternation, involves an unexpressed but understood object in the intransitive variant. However, the understood object here is interpreted as &quot;people&quot;.</Paragraph> <Paragraph position="26"> Emotional-reacti0n verbs are also set apart by another prop-erty: they only have passive nominals. Thus, the children's disappointment means that the children experienced disappointment (passive interpretation), not that they caused disappointment (active interpretation). Compare the interpretation of this nominal to that of the nominal related to a verb like describe: Gene's description can refer to the description that Gene gave (active interpretation) or to a description that someone gave of Gene (passive interpretation).</Paragraph> <Paragraph position="27"> We return to the example involving present discussed in the previous section. The alternation manifested by the verb present is exhibited by the members of a small class of verbs which also includes entrust, furnish, supply, and trust. The Present S-rule in (31) can be generalized so that it will apply to the entire class of verbs like present, which we name the present class.</Paragraph> <Paragraph position="28"> Since the verb supply is a member of the Present class, the SLrule in (57) applies and START will be able to answer the question correctly: (60) Yes, Mark supplied fresh vegetables to the restaurant. The examples described in this section show how the transparent syntax of the S-rules coupled with the information about verb class membership provided by the Lexicon facilitates fluent and flexible dialog between the user and the language processing system.</Paragraph> </Section> <Section position="7" start_page="519" end_page="521" type="metho"> <SectionTitle> 6. Employing S-Rules: Additional Examples </SectionTitle> <Paragraph position="0"> In this section we present additional dialogues that START can handle. These interchanges are chosen to illustrate the use of S-rules as well as the range of coverage of the START system.</Paragraph> <Paragraph position="1"> In the process we introduce S-rules that handle several more semantic-syntactic interdependencies, including the indeflrfite object and reflexive alternations discussed in the introduction to the paper.</Paragraph> <Paragraph position="2"> At the beginning of tim paper we presentcd two short interchange.,~ between two speakers of English and asked what it would take for a natural language system to be able to participate in these interchanges. We repeat the examples below: These examples were used to ilhlstrate some of the semantic-syntactic ;nterdependencies characteristic of verbs that partio ipate in bansitivity alternations. Verbs that have both transitive and i*ttransitive uses differ in the interpretations associated with their intransit, ive uses, as these interchanges were intended to show. S-rules allow START to answer questions that draw on knowledge of the semantic-syntactic interdependencies that are manitk~sted in transitivity alternations.</Paragraph> <Paragraph position="3"> The two transitivity alternations relevant to the interchanges above are the indefinite object alternation (with eat) and the reflexive alternation (with dress). When used intransitively, verbs thai. participate in the indefinite object alternation describe actions where there is an iml)lied object which is understood to be something that is a typical object of the verb. Thus: (63) Sally ate a pear. ==> Sally ate. (i.e., Sally ate some food or a meal.) We can formulate an S-rule that makes explicit the relationship between the transitive and the intransitive use of such verbs.</Paragraph> <Paragraph position="4"> Now if S':CAt~'F were asked &quot;Did Sally eat?&quot; after having been told that Sally ate a pear, START would be able to answer &quot;Yes&quot;, assunfing that ear's lexical entry indicates that it is a member st the activity/occupation class.</Paragraph> <Paragraph position="5"> In order to handle the dress interchange, START needs an S-rule that captures the properties of the reflexive alternation shown by verbs of grooming. The intransitive use of a verb that participates in this alternation implies that the subject perfornmd tl,e action denoted by the verb on himself or herself. (65) David dressed. --==> David dressed himself.</Paragraph> <Paragraph position="6"> We can capture the special interpretation associated with the intransitiw~ use of a verb like dress by means of an S-rule: Now if asked &quot;Did ~leff dress himself?&quot;, after being told that Jeff dresscd, tim system would answer &quot;Yes&quot; since the lexical entry of dress in(iicates that it is a verb of grooming. START will also be ablc to handle the following more complex interchange: (67) Input: Ann dressed and Mau'y dressed the baby.</Paragraph> <Paragraph position="7"> Question: Who dressed herself? STA.II!I': Ann dressed herself.</Paragraph> <Paragraph position="8"> Quest~ion: Who dressed the baby? START: Mary dressed the baby.</Paragraph> <Paragraph position="9"> Oueation: Did Mary dress? STAt/T: I don't know.</Paragraph> <Paragraph position="10"> In a more complex situation, S-rules are allowed to trigger each other and to ask each other for help. To exemplify this, we introduce two more S-rules. These rules are used to capture the properties of a cl~s of verbs that has received substantial attention in the linguistics literature (see Anderson \[1971\], Fraser \[19711, Schwarz-Norman \[1976\], Jeffries and Willis \[1984\], Rappaport and Levin \[1986\], among othms). We refer to this class as tile spray/load class after two prototypicM members. The verbs in this class describe actions that involve putting substances or materials on surfaces (e.g. spray) or in containers (e.g. load). What distinguishes one member of the spray/load class from another is the manner in which this action is performed. Some members of this class are listed below: (68) load, pack, pile, smear, spray, spread, stack, stuff, wrap, ... All the members of this class display an argument alternation, the locative alternation; they arc.&quot; found in two syntactic frames, as illustrated below: (69) Miriam sprayed paint on tl~e wall. (locative variant) (70) Miriam sprayed the wall with paint. (with variant) (71) Jan loaded hay on the truck.</Paragraph> <Paragraph position="11"> (72) ,Jan loaded the truck with hay.</Paragraph> <Paragraph position="12"> Although the sentences in each of these pairs might appear at first glance to be paraphrases, they are not. For instance, in the first pMr, only (69), the locative w~riant, may be used to describe a situation where a small portion of the. wall is covered wifll paint as a result of the spraying (the partitive interpretation), while sentence (70), the with variant, necessarily implies ~hat, the wall is entirely covered with paint as a result of the action (the holistic interpretation). The sentences in each pair ~'~rc near paraphrases in the sense that the truth of the with variant entails the truth of the locative varimlt, but not vice versa.</Paragraph> <Paragraph position="13"> Before we can ibrmulate an S-rule that captures ~;lle relation between the two variants, we need to look more closely at the locative variant. When used in the locative variant, apray/load verbs are found with a wide range of locative prepo.,;itions (tlm set of prepositions indicating spatial relationships).</Paragraph> <Paragraph position="14"> (73) Miriam sprayed paint on/under/around the table.</Paragraph> <Paragraph position="15"> When the .with variant of a spray/load verb is part,phrased by the locative variant, typically only one d the range of locative prepositions is possible; the choice is a function of the verb involved. The verbs stray and load both involve the preposition on in the locative variant, but the w.wb stuff requires the prepo.sition into, while the verb wrap requires around: (74) Jessiea stuffed the pillow with feattmrs.</Paragraph> <Paragraph position="16"> (75) aessica stuffed the feathers into tile pillow.</Paragraph> <Paragraph position="17"> (76) Oabriella wrapped tile package with paper.</Paragraph> <Paragraph position="18"> (77) Gabriella wrapped paper around the package.</Paragraph> <Paragraph position="19"> It appears that each spray~load verb is associated with a default locative preposition that must be indicated in its lexical entry: (78) (spray :verb :spray-load :defimlt.-preposition on) (79) (wrap :verb :spray-load :default-preposition around) (80) (stuff :verb :spray-load :default-preposition into) We can now write an S-rule that captures the relation be tween the with and locative variants of the locative alternation: (8:1.) Itolistie/pariitive S-rule If <<subject verb object1> with object2> Then <<subject verb object2> prepLoc object1> Provided verb (? spray/load class In this S-rule tile variable ~prepLoc' is instantiated with the default locative preposition associated with tile verb tim rule is applying to.</Paragraph> <Paragraph position="20"> Using the Holistic/partitive S-rule, START is easily able to handle the following interchanges: (82) Input: Matilda stuffed the suitcase with books.</Paragraph> <Paragraph position="21"> Question: Did Matilda stuff the books into the suitcase? START: Yes, Matilda stuffed the books into the suitcase.</Paragraph> <Paragraph position="22"> (83) Input: Miriam wrapped the book with paper.</Paragraph> <Paragraph position="23"> Question: Who wrapped the paper around the book? START: Miriam wrapped the paper around the book.</Paragraph> <Paragraph position="24"> To illustrate how S-rules can trigger each other, we introduce an S-rule motivated by another property of spray~load verbs. We have seen that the with variant of a spray~load verb entails the locative variant. In addition, the locative variant, which describes the placement of some substance in a container or on some surface, implies that the substance will be in the containcr or on the surface. That is, sentence (84) entails sentence We attribute the entailment relationship between the two variants described by the Holistic/partitive S-rule to a speaker's linguistic knowledge, while the entailment relationship described by the Resulting Location S-rule reflects real world knowledge. We introduced the Resulting Location S-rule in order to show how one S-rule can operate on the output of another. The Holistic/partitive S-rule and the Resulting Location S-rule together allow sentence (88) to be deduced fl'om sentence (87): (87) ,\]an loaded the truck with hay.</Paragraph> <Paragraph position="25"> (88) Hay was on the truck.</Paragraph> <Paragraph position="26"> By using both these S-rules, the system can handle the following interchange: (89) Input: Jan loaded the truck with hay.</Paragraph> <Paragraph position="27"> Question: Was there hay on the truck? START: Yes, there was hay on the truck.</Paragraph> <Paragraph position="28"> The syntactic component of START is able to undo the effects of the There-Insertion rule in the question, translating it into the T-expression (90) <<hay be> on truck> This T-expression does not match anything in the knowledge base, so START tries to apply S-rules. The Resulting Loca-tion S-rule used in the backward mode can apply to this Texpression, suggesting that START then searches for the T-expression that matches template (91) below, where the variable verb is restricted to members of the spray/load class.</Paragraph> <Paragraph position="29"> (91) <<subject verb hay> on truck> This T-expression also does not match against anything in the this rule to the sprayfload verbs. Actually this S-rule applies to a larger set of verbs, the set of causative verbs of change of location, including put, place, insert and transitive move, slide, drop, as well as the sprayfload verbs. For instance, if Rebecca put the books on the table, then the books are on the table. We have given a restrictive formulation of the PROVIDED clause in this S-rule since we do not want to discuss the implications of introducing a hierarchy of verb class types for the operation of S-rules. obtained from the Input sentence in (89), allowing START to generate the appropriate answer.</Paragraph> <Paragraph position="30"> Below we show some further examples of interchanges successfully handled by START through the use of additional Srules. These examples involve verbs of manner of motion, listed in (94), and verbs of creation, listed in (97): (94) climb, cross, fly, gallop, jump, march, swim, walk, ... (95) Input: Albert jmnped over the fence.</Paragraph> <Paragraph position="31"> Question: Did he jump the fence? START: Yes, Albert jumped the fence.</Paragraph> <Paragraph position="32"> (96) Input: Candy climbed up the mountain.</Paragraph> <Paragraph position="33"> Question: What did Candy climb? START: Candy climbed the mountain.</Paragraph> <Paragraph position="34"> (97) bake, carve, croche~ hammer, knit, sew, weave, ...</Paragraph> <Paragraph position="35"> (98) Input: Jessica baked the dough into round loaves.</Paragraph> <Paragraph position="36"> Question: What did Jessica bake round loaves from? START: Jessica baked round loaves from ~he dough.</Paragraph> <Paragraph position="37"> (99) Input: Miriam carved wood into a doll.</Paragraph> <Paragraph position="38"> Question: Out of what did Miriam carve the doll? START: Miriam carved the doll out of the wood.</Paragraph> <Paragraph position="39"> At any given moment many S-rules may be hidden in the computer's memory examining the output flow generated by START and waiting tbr their turn to participate in the deduction process. S-rules fundamentally expand the power of our system; they open a window into the intricate world of semantic-. syntactic interactions.</Paragraph> </Section> class="xml-element"></Paper>