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<?xml version="1.0" standalone="yes"?> <Paper uid="E93-1025"> <Title>A Discourse Copying Algorithm for Ellipsis and Anaphora Resolution</Title> <Section position="3" start_page="0" end_page="204" type="metho"> <SectionTitle> 2 Past Approaches </SectionTitle> <Paragraph position="0"> Much attention has been paid to the ellipsis problem in linguistics (e.g., \[Dahl, 1972; Dahl, 1974; Fiengo and May, 1990; Gawron and Peters, 1990; Ha'lk, 1987; Hellan, 1988; Klein, 1987; Lappin, 1992; Sag, 1976; Williams, 1977\]), as well as in natural language processing (e.g., \[Dalrymple et al., 1991; Hardt, 1992; Lappin and McCord, 1990; Priist et al., 1991\]). We begin by briefly pointing out several problems with some of these approaches.</Paragraph> <Paragraph position="1"> .Syntactic accounts of ellipsis (e.g., \[Fiengo and May, 1990; Ha'/k, 1987; Hellan, 1988; Lappin, 1992; Lappin and McCord, 1990\]) posit the copying of syntactic structure from the source clause representation to the target clause representation. 2 Such accounts fail to explain certain empirical facts. First, an active voice target clause can obtain its representation from a passive voice source clause (and vice versa), but in these cases there is no surface syntactic VP constituent to copy: (2) A lot of this material can be presented in a fairly informal fashion, and often I do. (from text of \[Chomsky, 1982\]) (3) This problem was to have been looked into, but obviously nobody did.</Paragraph> <Paragraph position="2"> (Vincent Della Pietra, in conversation) Second, an elliptical clause can obtain its referent from an event representation evoked into the discourse world by a nominalization: (4) Meanwhile, they sense a drop in visitors to the city. Those who do, they say, axe not taking cabs. (Chicago Tribune, courtesy Gregory Ward) Third, an elliptical clause may have multiple antecedents: null (5) Mary wants to go to Spain and Fred wants to go to Peru, but because of limited resources, only one of them can.</Paragraph> <Paragraph position="3"> (\[Webber, 1978\]) Several semantic accounts of ellipsis utilizing logical forms have been proposed. Following Dalrymple et at. \[1991\], we distinguish between identity-of-relations analyses (e.g., \[Gawron and Peters, 1990; Klein, 1987; Sag, 1976; Williams, 1977\]) and non-identity analyses \[Dalrymple et al., 1991\]. Identity-of-relations analyses treat source clauses as having ambiguous derivations, with target clauses receiving one such derivation. For example, the source clause in example (1) would have derivations that (at some level) lead to the following two interpretations: John has the property P where: (6) P = likes John's mother (7) P = likes his own mother These two properties lead to the same reading for the source clause. However, under an identity-of-relations analysis, if the target receives a strict interpretation, then necessarily (6) is the correct interpretation for the source, whereas if the target receives a sloppy reading, then (7) is necessarily the correct interpretation.</Paragraph> <Paragraph position="4"> The following example, from Dalai \[1972\], is termed as n case of cascaded ellipsis in Dalrymple, et al. \[1991\], and is problematic for identity-of-relations analyses: 2Fiengo and May \[Fiengo and May, 1990\] actually posit copying of LF representations, but their analysis shares the problems of the syntactic accounts.</Paragraph> <Paragraph position="5"> (8) John realizes that he is a fool, but Billi does not, even though hisi wife does.</Paragraph> <Paragraph position="6"> An acceptable, and perhaps preferred, reading for (8) is: (9) John realizes that John is a fool, but Bill does not realize that Bill is a fool, even though Bill's wife realizes Bill is a fool. Example (8) contains two cases of ellipsis; the reading in (9) results from the second clause receiving a sloppy interpretation from the first and the third clause receiving a strict interpretation from the second. An identity-of-relations analysis, however, specifically predicts that the reading given in (9) does not exist. Because the second clause will have the sloppy derivation received from the first, the strict derivation that the third clause requires from the second will not be present.</Paragraph> <Paragraph position="7"> Dalrymple, et at. \[1991\] (henceforth DSP) give an account of ellipsis resolution based on an equational analysis utilizing higher-order unification. Unlike identity-of-relations accounts, no unmotivated ambiguity is predicated to exist for VPs with pronouns, instead the ambiguity exists in the resolution process. As a result, reading (9) for sentence (8) is correctly predicted to exist.</Paragraph> <Paragraph position="8"> We step through DSP's analysis of example (1) to acquaint the reader with their system. The representation of the source clause in this example is: (10) likes(John, mother_of(John)) The ellipsis is resolved by deriving a property P such that xepresentation (10) results from applying it to John:</Paragraph> <Paragraph position="10"> This derivation yields two solutions: 3</Paragraph> <Paragraph position="12"> To generate a reading for the target clause, either one of these representations may he applied to the element in the target clause that is parallel to John, in this case Bill. 4. Applying relation (12) to Bill yields the strict reading in (14); applying relation (13) yields the sloppy reading given in (15):</Paragraph> <Paragraph position="14"> While the DSP account is comprehensive, some flaws remain. First, they claim that their analysis covers cases of stripping as well as ellipsis. Consider the case of stripping in example (16): (16) John likes his mother, and Mary's too. This sentence has only the following reading: (17) John likes John's mother, and John likes Mary's mother.</Paragraph> <Paragraph position="15"> The representation for the source clause in DSP's system is given by: (lS) likes(John, mother_of(John)) After deriving two possible properties P, either of the two readings given in (19) and (20) may be derived:</Paragraph> <Paragraph position="17"> Reading: Mary likes Mary's mother However, only reading (19) actually exists for sentence (16), so DSP's system overgenerates in this case. We return to this example in Section 4.3.</Paragraph> <Paragraph position="18"> Second, to account for missing reading examples and the 5-reading sentence, DSP postulate an unspecified &quot;suitable definition of generalized antecedent linking&quot;, and need to impose an additional constraint on property derivation requiring that when an element is abstracted over, so must its generalized antecedent. These examples are discussed in Sections 4.4 and 4.5. Third, their account does not extend cleanly to similar phenomena at the noun phrase level, a topic we return to in Section 5.</Paragraph> </Section> <Section position="4" start_page="204" end_page="205" type="metho"> <SectionTitle> 3 An Event Representation and Algorithm </SectionTitle> <Paragraph position="0"> We seek an analysis of ellipsis that preserves the advantages of the DSP analysis but remedies the problems we have noted. The following sections describe the event representation and the resolution algorithm that embody our analysis.</Paragraph> <Section position="1" start_page="204" end_page="204" type="sub_section"> <SectionTitle> 3.1 Event Representation </SectionTitle> <Paragraph position="0"> To highlight the general mechanism of our approach we will utilize a simple, Davidsonian-style data structure where events are reified as variables \[Davidson, 1967\]. For example, sentence (21) will be represented by the feature-based event structure in (23); this corresponds to the more standard logical form given in (22)5: (21) John sees Mary (22) el: see(et), agent(el, John), theme(el, Mary) 5We use semantic role names llke agent and theme somewhat loosely.</Paragraph> <Paragraph position="1"> (23) el: \[ predicate: see agent: John theme: Mary \] An additional requirement that we impose on the representation is what we term role linking. In order to link referential elements to their referents, functions are used to retrieve the value of roles in event structures. For example, the function agent(el) would be used to link a referential element to John in the representation of el in (23), likewise theme(et) would be used to link to Mary. Consider sentence (24) and its event structure (25): (24) Johni likes hisi mother (25) e2: \[predicate: likes agent: John theme: \[obj: mother po : agent(e ) \] \] The representation of his is a function bearing the role of the entity it refers to in the sentence. In contrast to the DSP approach, the representation for a full NP will appear in an event structure only when it is explicitly mentioned in the discourse, e.g., John would have appeared twice in (25) only if sentence (24) were John likes John's mother.</Paragraph> <Paragraph position="2"> When there are several referential elements in a clause that are coreferential, each has several potential antecedents with which role linking may be established. The following rule governs role linking in this case: (26) A referential element is linked to the most immediate coreferential element that c-commands it in the syntax.</Paragraph> <Paragraph position="3"> Because we encode the distinction between full NPs and the referential elements that refer to them, we naturally account for the stripping example, as discussed in Section 5.3. The fact that we link referential elements through the role that their referents have in their events, in conjunction with rule (26), allows us to account for the missing readings examples discussed in Section 5.4 and the 5.reading example discussed in Section 5.5 without appeal to any additional constraints on the algorithm. We describe this algorithm in the next section.</Paragraph> </Section> <Section position="2" start_page="204" end_page="205" type="sub_section"> <SectionTitle> 3.2 Resolution Algorithm </SectionTitle> <Paragraph position="0"> The discourse copying algorithm utilized by ellipsis resolution is summarized as follows: 1. Identify the source clause representation and formulate a parallel structure with unfilled roles and modifiers for the target.</Paragraph> <Paragraph position="1"> 2. Fill roles with entities given in the target clause. 3. Copy remaining empty role fillers from the source: (a) Identify parallel elements, i.e., the objects in the source representation corresponding to the empty roles in the target, s (b) All role fillers may be (i) referred to, where the appropriate function is used to link the role filler to the corresponding object in the source representation. In the case that the role filler is a function with a link to the source event, it may also be (ii) copied, where a new instantiation of the function is created and the source event variable is replaced with its corresponding parallel target event variable.</Paragraph> <Paragraph position="2"> As with the DSP analysis, this discourse copying method does not assume any ambiguity in the source clause. Ambiguities are generated by the choice given in Step 3b; referring will result in a strict reading and copying will result in a sloppy reading.</Paragraph> </Section> </Section> <Section position="5" start_page="205" end_page="209" type="metho"> <SectionTitle> 4 Treatment of Phenomena </SectionTitle> <Paragraph position="0"> We show how our algorithm accounts for a variety of ellipsis phenomena. Section 4:1 illustrates that a basic case of strict and sloppy ambiguity is correctly analyzed. Section 4.2 shows that the algorithm accounts for the cascaded ellipsis case, thereby retaining the advantages of the DSP approach over identity-of-relations anMyses. The remaining subsections show how our algorithm accounts for the cases cited as problematic for DSP in Section 2. Section 5 will then move beyond VP-ellipsis to discuss how related non-elliptical phenomena are accounted for.</Paragraph> <Section position="1" start_page="205" end_page="205" type="sub_section"> <SectionTitle> 4.1 Ellipsis </SectionTitle> <Paragraph position="0"> We consider example (1) again, renumbered as (27): (27) John likes his mother and Bill does too.</Paragraph> <Paragraph position="1"> The representation for the source clause in (27) is: (28) el: \[ predicate: like agent: John theme: \[obj: mother poss: agent(el) \]\] The parallel event for the target is constructed (Step 1), and Bill is added as the agent (Step 2): (29) e2: \[ predicate: agent: Bill theme:\] Step 3b can only refer to the value of the predicate role. 7 Since the theme of the source event contains a referential link to the source event itself, Step eWe follow the DSP analysis in distinguishing the process of determining paxallel elements from the process of performing resolution. For all of the examples considered in this paper, one can assume that parallel elements share the same thematic role in their respective events. The reader is advised to consult Dalrymple et al. \[1991\] for arguments on why this constraint should be broadened. 7Step 3a of the algorithm as stated requires that a function be used when referring, i.e., technically we should fill the predicate role of e2 with the function predicate(el). To improve readability, when the object 3b allows the theme to either be referred to with a function or copied by creating a new instantiation of the function occupying thetheme and replacing the event variable el with its parallel event variable e2. Referring to the theme role of el yields the strict reading in (30): (30) e2: \[predicate: like agent: Bill theme: theme(el) \] Reading: Bill likes John's mother Conversely, copying the theme role of el yields the sloppy reading in (31): (31) eu: \[ predicate: like agent: Bill theme: \[obj: mother poss: agent(e2) \] \] Reading: Bill likes Bill's mother Note that if the his in sentence (27) refers to the mother of someone in external discourse, say agent(co), then Step 3b must leave this intact since the link is not to the source event. This correctly yields the only available reading for the target clause in this case.</Paragraph> </Section> <Section position="2" start_page="205" end_page="206" type="sub_section"> <SectionTitle> 4.2 Cascaded Ellipsis </SectionTitle> <Paragraph position="0"> We show how our analysis accounts for the cascaded ellipsis case discussed in Section 2, repeated below: (32) John realizes that he is a fool, but Billi does not, even though hisi wife does.</Paragraph> <Paragraph position="1"> In particular, we work though the derivation of example (32) that leads to reading (33), the reading that is problematic for identity-of-relations analyses: (33) John realizes that John is a fool, but Bill does not realize that Bill is a fool, even though Bill's wife realizes Bill is a fool.</Paragraph> <Paragraph position="2"> The representation for the first clause in (32) is given in (34): (34) el: \[ predicate: realize agent: John theme: e2: \[ predicate: be agent: agent(el) be_pred: fool \] \] The clause represented by (34) is the source for the elided second clause. To obtain the desired reading, Step 3b chooses the copy option to yield the sloppy reading. A new function is instantiated from the function in the agent role of e2, and the event variable el is replaced with its parallel event variable ca: being referred to contains no links to any event, we will opt to reproduce the value in the role of the target representation rather than use a function.</Paragraph> <Paragraph position="3"> (35) es: \[predicate: realize polarity: negative agent: Bill theme: e4: \[predicate: be agent: agent(el) be_pred: fool \] \] Reading: Bill does not realize that Bill is a fool The clause represented by (35) is the source for the elided third clause. In this case Step 3b chooses to refer to the agent of e4 using a function, deriving the strict reading: s (36) e~: \[predicate: realize agent: \[ obj: wife poss: agent(ea) \] theme: e6: \[predicate: be agent: agent(e4) be_pred: fool \] \] Reading: Bill's wife realizes that Bill is a fool Thus, the reading not derivable by identity-of-relations analyses is derivable by our algorithm.</Paragraph> </Section> <Section position="3" start_page="206" end_page="206" type="sub_section"> <SectionTitle> 4.3 Stripping </SectionTitle> <Paragraph position="0"> We now show how our analysis accounts for the stripping example discussed in Section 2, repeated below: (37) John likes his mother, and Mary's too.</Paragraph> <Paragraph position="1"> In the DSP analysis it is possible to derive the following non-existent reading: (38) John likes John's mother, and Mary likes Mary's mother.</Paragraph> <Paragraph position="2"> Our algorithm generates only the correct reading for sentence (37). The representation for the source clause in example (37) is: (39) el: \[predicate: like agent: John theme: \[obj: mother poss: agent(el)\]\] To derive a representation for the target clause, we create a parallel event structure (Step 1) and fill in the parallel element representation for Mary (Step 2). The remaining empty role fillers are that for the predicate and agent roles. Since neither contains a link to the source event, Step 3b only has the option of referring to it: (40) e2: \[predicate: like agent: John theme: \[obj: mother poss: Mary \] \] Reading: John likes Mary's mother SWe omit the feature for polarity when it is positive, as in representation (36).</Paragraph> <Paragraph position="3"> This yields the correct interpretation for the target clause. Because Mary is parallel to the only element in the source clause that contains a role link in example (37), Step 3b(ii) of the algorithm is never entered. As a result, no ambiguity exists and therefore (40) is the only derivable reading.</Paragraph> </Section> <Section position="4" start_page="206" end_page="207" type="sub_section"> <SectionTitle> 4.4 Missing Readings </SectionTitle> <Paragraph position="0"> One might expect that for source VPs which contain N referring elements, 2 N readings would be possible. However, Dahl \[1974\] noticed that the following example has only three readings, not four: (41) Bill believed that he loved his wife, and Harry did too.</Paragraph> <Paragraph position="1"> Out of the expected readings (42a)-(42d), reading (42d) is missing: (42a) Harry believed that Bill loved Bill's wife. (42b) Harry believed that Harry loved Harry's wife.</Paragraph> <Paragraph position="2"> (42c) Harry believed that Harry loved Bill's wife. (42d) # Harry believed that Bill loved Harry's wife.</Paragraph> <Paragraph position="3"> Because he c-commands his in sentence (41), by rule (26) the pronoun his is linked to the pronoun he instead of directly to Bill. The event structure for the source clause in (41) is therefore: (43) el: \[ predicate: believe agent: Bill theme: e2: \[ predicate: love agent: agent(el) theme: \[obj: wife poss: agent(e2)\]\]\] We show that the reading in (42d) is correctly predicted not to exist by applying the algorithm to derive all possible readings for the target clause. Step 3b will have two options for each of the role links occupying the agent role of e~ and the poss role of the theme role of e2, resulting in a total of four possibilities. In the first case both objects are referred to, yielding the all-strict reading given in (42a): (44) el: \[predicate: believe agent: Harry theme: el: \[predicate: love agent: agent(e2) theme: theme(e~) \] \] Reading: Harry believed that Bill loved Bill's wife In the second case both functions are copied, yielding the all-sloppy reading given in (42b): (45) e3: \[ predicate: believe agent: Harry theme: e4: \[predicate: love agent: agent(e3) theme: \[obj: wife</Paragraph> <Paragraph position="5"> Reading: Harry believed that Harry loved Harry's wife In the third case the function occupying the agent role of e2 is copied and the structure occupying the theme role of eg. is referred to, yielding reading (42c): (46) ca: \[predicate: believe agent: Harry theme: e4: \[predicate: love agent: agent(ca) theme: theme(e~) \] Reading: Harry believed that Harry loved Bill's wife These three readings are the acceptable ones for the target clause in (41). The algorithm also allows for a fourth possibility in which the function occupying the agent role of eg. is referred to, and the function occupying the theme role of e2 is copied: (47) ca: \[predicate: believe agent: Harry theme: e4: \[ predicate: love agent: agent(e2) theme: \[obj: wife poss: agent(e4)\]\]\] Reading: Harry believed that Bill loved Bill's wife In this case the function agent(e4) returns the value of agent(e2), namely Bill, which again yields the all-strict reading in (42a). Thus, the non-existent reading given in (42d) is not derivable by the algorithm. This behavior is a result of our choosing to link referential elements to their referents via their roles in the event structures. During the derivation of the representation given in (47), we chose the sloppy option for the second referential element. Instead of replacing the element itself with its parallel element in the event structure as other accounts do, we replaced it with a link to its parallel role in its parallel event. This process was &quot;sloppy&quot; in th~ the resulting link is to the agent of e4 instead of to the agent of ez; however, the resulting effect is &quot;strict&quot; because the agent of e4 is the same as e2 in this case. The use of c-command as the linking criterion explains why m~ny speakers get all four expected readings in (48) and (49), which ~re otherwise very similar to (41): (48) Bill believed that his wife loved him, and Harry did too.</Paragraph> <Paragraph position="6"> (49) Bill believed that his wife loved his brother, and Harry did too.</Paragraph> <Paragraph position="7"> Since neither pronoun c-commands the other in these cases, the event structure for the source clause would have both directly linked to Bill, and the algorithm would derive all four readings for the target clause. Sag \[1976\] however notes that example (50) only has three readings: (50) Edith said that finding her husband nude had upset her, and Martha did too.</Paragraph> <Paragraph position="8"> Out of the four possibilities for the target, reading (51) is missing: (51) # Martha said that finding Martha's husband nude had upset Edith.</Paragraph> <Paragraph position="9"> The algorithm derives only the three correct readings if the first instance of her in sentence (50) is linked to the second. Since neither pronoun c-commands the other, this violates the linking rule. However, as noted by Reinhart \[1983\] (pp. 179180), &quot;experiencing&quot; verbs such as upset often pose problems for linguistic analyses utilizing c-command. What is required for the linking rule is a notion of a reflexive context that applies to pronouns of all cases (for which c-command is an imperfect approximation). For instance, the NP her husband in sentence (50) is in a reflexive context, i.e., replacing 'her husband' with an accusative pronoun referring to Edith requires the reflexive form: (52) ... finding herselfi/*heri nude had upset her/. Because the her in her husband in sentence (50) is in the reflexive context of the second her, the corresponding role link is required in the event structure. Thus, the algorithm works correctly for example (50); the flaw arises from using c-command in the linking rule to model reflexive contexts. In the future we expect to revise the linking rule by adopting rules superseding c-command for predicting reflexivization. The question of whether our analysis can be considered purely semantic rests on the question of whether reflexivization is syntactically or semantically controlled.</Paragraph> </Section> <Section position="5" start_page="207" end_page="209" type="sub_section"> <SectionTitle> 4.5 5-Reading Example </SectionTitle> <Paragraph position="0"> DSP discuss the following example (from Gawron and Peters \[1990\]) as a point of departure among previous analyses: (53) John revised his paper before the teacher did, and Bill did too.</Paragraph> <Paragraph position="1"> DSP claim that this sentence has five readings; we agree. The DSP analysis as presented derives six readings. To obtain only the correct five readings, they appeal to the constraint on abstracting over generalized antecedents mentioned in Section 2; however, a precise method for linking elements to generalized antecedents is not given. Our algorithm generates only the correct five readings without appeal to any additional constraints or processes.</Paragraph> <Paragraph position="2"> The notably absent reading for the third clause in this case is given in (54): (54) Bill revised John's paper before the teacher revised Bill's paper.</Paragraph> <Paragraph position="3"> We step through the derivation of some of the readings to show that no representation for reading</Paragraph> <Paragraph position="5"> (54) is derived. 9 We first derive the possible readings for the first ellipsis. The representation for the source clause is: (55) el: \[predicate: revise agent: John theme: \[ obj: paper poss: agent(el) \] \] We add the temporal modifier, parallel event structure, and role fillers for the representation of the first elided clause: (56) ex: \[predicate: revise agent: John theme: \[obj: paper poss: agent(el) \] time: \[relation: before obj: e2: \[predicate: revise agent: teacher theme:\] \] \] The filler of the theme role in the representation for the source event contains a link to that, so there are two options. First, the theme may be referred to, yielding the strict reading: (57) el: \[ predicate: revise agent: John theme: \[obj: paper poss: agent(el) \] time: \[ relation: before obj: e2: \[predicate: revise agent: teacher theme: theme(et)\]\]\] Reading: The teacher revised John's paper Alternatively, the theme may be copied, yielding the sloppy reading: (58) el: \[predicate: revise agent: John theme: \[obj: paper poss: agent(el) \] time: \[relation: before obj: e2: \[ predicate: revise agent: teacher theme: \[ obj: paper poss: agent(e2)\]\]\] Reading: The teacher revised the teacher's paper We now consider the readings for the second ellipsis. Unlike the case of cascaded ellipsis, in this example the second ellipsis has the entire conjoined clause as its source. We first consider the readings derived from the strict reading represented in (5?). 9We thank an anonymous reviewer for pointing out a flaw with a slightly different manifestation of this account in predicting the correct readings for this example. In each of the following derivations event ea is parallel to event el, and event e4 is parallel to e2. After the event and non-referential role information is copied, there are four options. Referring to both of the roles containing links to a source event results in the all-strict reading: (59) ca: \[ predicate: revise agent: Bill theme: theme(et) time: \[relation: before obj: e4: \[predicate: revise agent: teacher theme: theme(e~)\]\]\] Reading: Bill revised John's paper before the teacher revised John's paper In the second possibility, both roles can be copied, resulting in the all-sloppy reading: (60) ca: \[ predicate: revise agent: Bill theme: \[ obj: paper poss: agent(ca) \] time: \[ relation: before obj: e4: \[ predicate: revise agent: teacher theme: theme(ca) \]\]\] Reading: Bill revised Bill's paper before the teacher revised Bill's paper Third, the poss role of the theme role of el may be copied and the theme role of e2 may be referred to: (61) ca: \[ predicate: revise agent: Bill theme: \[obj: paper poss: agent(ez) \] time: \[ relation: before obj: e4: \[ predicate: revise agent: teacher theme: theme(e2) \]\]\] Reading: Bill revised Bill's paper before the teacher revised John's paper Finally, the poss role of the theme role of el may be referred to and the theme role of e2 may be copied: (62) ca: \[ predicate: revise agent: Bill theme: theme(el) time: \[ relation: before obj: e4: \[predicate: revise agent: teacher theme: theme(ea) \]\]\] Reading: Bill revised John's paper before the teacher revised John's paper Note that the reading (62) is the same as the strict/strict reading in (59). Thus, the algorithm so far has generated three readings and has not generated the non-existent reading (54). We leave it to the reader to determine that when using representation (58) as the source for the final ellipsis, the following two readings are generated: (63) Bill revised John's paper before the teacher revised the teacher's paper (64) Bill revised Bill's paper before the teacher revised the teacher's paper The algorithm therefore derives all and only the correct five readings for example (53). Recall that the algorithm's ability to avoid the non-existent reading in the missing reading cases in Section 4.4 was due to our choosing to link referential elements to their referents through their roles in the event structures. In that case, the critical point that implicitly eliminated the missing reading occurred during a derivation where the sloppy option was chosen. The ability of the algorithm to avoid generating the missing reading given in sentence (54) is also due to our role linking scheme, but in this case the crucial step was in choosing the strict option in the derivation of the first ellipsis (which resulted in representation (57)). Because the algorithm referred to the theme of el through its role instead of replicating it, the representation for the non-existent reading could not be derived during the resolution of the second ellipsis. In either case, accounts that rely on parallel elements cannot avoid these readings without appeal to additional constraints. Our analysis requires no such constraints; the correct readings naturally result from the mechanism itself.</Paragraph> </Section> </Section> <Section position="6" start_page="209" end_page="209" type="metho"> <SectionTitle> 5 Beyond Ellipsis </SectionTitle> <Paragraph position="0"> The hallmark of the discourse copying process is the need not only to refer to a previously mentioned entity or event, but to create a new instantiation of it.</Paragraph> <Paragraph position="1"> VP-ellipsis is one such process; the meaning of the source clause serves as both the referent and the object from which a new, more general instantiation is created. Here we claim that the strict/sloppy distinction is an inherent property of the discourse copying process, and therefore not of VP-ellipsis resolution alone. Our algorithm is directly applicable to a wide variety of discourse copying phenomena at both the VP and NP levels.</Paragraph> <Paragraph position="2"> All of the following reference phenomena require discourse copying, and therefore exhibit strict/sloppy ambiguities: * VP-ellipsis: John likes his mother, and Bill does too.</Paragraph> <Paragraph position="3"> * Pronominal Event Anaphora: John got shot by his father. That happened to Bob too.</Paragraph> <Paragraph position="4"> * Definite Event Anaphora: John kissed his wife, and Bill followed his example. (\[Dalai, 1972\]) * 'Only': Only John loves his mother.</Paragraph> <Paragraph position="5"> s 'One' Anaphora: Although John bought a picture of his son, Bill snapped one himself.</Paragraph> <Paragraph position="6"> Definite NPs and pronouns referring to NPs may also require discourse copying in certain restricted contexts. In these cases, strict/sloppy ambiguities are present: * Definite NPs: John actually remembered his wife's birthday. Most men forget this important date.</Paragraph> <Paragraph position="7"> * 'Lazy' Pronouns: The man who gives his paycheck to his wife is wiser than the man who spends it. (\[Karttunen, 1969\]) We make a distinction between the processes of determining when discourse copying applies and performing the resolution. We show how the representations of some of these examples are resolved by our algorithm.</Paragraph> <Paragraph position="8"> The pronouns it and that can be used to refer to events. Usage such as that in example (65) requires discourse copying since a new instantiation of an existing event needs to be applied to the target representation: null (65) John got shot by his father.</Paragraph> <Paragraph position="9"> That happened to Bill too.</Paragraph> <Paragraph position="10"> That is, both (66) and (67) are possible readings for (65): (66) Bill was shot by John's father.</Paragraph> <Paragraph position="11"> (67) Bill was shot by Bill's father.</Paragraph> <Paragraph position="12"> These readings are generated by the algorithm in analogous fashion to the ellipsis cases. The representation for the source clause is: (68) el: \[ predicate: shot agent: \[ obj: father poss: theme(el) \] theme: John \] Assuming that Bill is the theme of the target event, referring to the agent role yields the representation for the strict reading in (69), and copying it yields the representation for the sloppy reading in (70): suffers from the same delusion.</Paragraph> <Paragraph position="13"> (72) John kissed his wife. Bill followed his example.</Paragraph> <Paragraph position="14"> Once the VP in the targets are recognized as event referential, the algorithm readily applies to these cases (with parallel elements being John and BilO, as it does not require any degree of syntactic or logical form parallelism between the referring expression and the referent.</Paragraph> <Paragraph position="15"> 'One' anaphora also requires a form of discourse copying, although for discourse entities instead of events: (73) John bought a picture of his son, while Bill snapped one himself.</Paragraph> <Paragraph position="16"> Most speakers find sentence (73) to be ambiguous between strict and sloppy readings, i.e., one could refer to a picture of John's son or of Bill's son. 1deg By applying our analysis to the discourse copying of objects, these readings naturally result. The source representation for sentence (73) is given in (74): (74) el: \[ predicate: bought agent: John theme: \[ obj: picture arg: \[ obj: son po : agent(el) \] \]\] We construct a representation for the information given in the target sentence without the 'one'anaphoric NP: (75) e2: \[predicate: snap agent: Bill theme:\] Step 3b of the algorithm operates in the same way as it does for ellipsis; the two choices yield the strict reading in (76) and the sloppy reading in (77): (76) e2: \[predicate: snap agent: Bill theme: theme(et) \] (77) e2: \[predicate: snap agent: Bill theme: \[obj: picture arg: \[ obj: son poss: agent(e2) \] \]\] As our analysis predicts, no sloppy reading exists if his refers to an intersentential discourse object (e.g., Fred).</Paragraph> <Paragraph position="17"> The cases involving definite NPs and lazy pronouns can be handled in an analogous fashion to 'one'-anaphora. However, whereas 'one'-anaphora by its nature involves discourse copying, the contexts in which definite NPs and pronouns can copy as well 1degObtaining the sloppy reading may be aided by considering a relevant context, such as one where John's and Bill's families are on vacation together.</Paragraph> <Paragraph position="18"> as refer are more limited. Therefore, the challenge in handling these latter cases is in determining when discourse copying is licensed.</Paragraph> <Paragraph position="19"> As a final note, we point out that the class of what we have termed referential elements can be extended to include implicit arguments as well as pronouns. For instance, consider examples (78) and (79), adapted from Partee \[1989\]: (78) John went to a local bar to watch the Superbowl, and Bob did too.</Paragraph> <Paragraph position="20"> (79) George drove to the nearest hospital, and Fred did too.</Paragraph> <Paragraph position="21"> In sentence (78), local has an implicit argument that is linked to John, likewise for nearest and George in sentence (79). In each case there are strict and sloppy readings for the target clause; e.g., the target in example (78) can mean that Bob went to the bar local to John, or a bar local to himself. As is the case with pronouns, if the implicit argument is instead linked to external discourse (e.g., &quot;local&quot; interpreted as being to the speaker instead of to John in sentence (78)), then there is only one reading for the target; no sloppy reading exists.</Paragraph> <Paragraph position="22"> Nominalizations can also contain implicit arguments that give rise to strict and sloppy readings, as in example (80): (80) The CS500 final exam is tomorrow.</Paragraph> <Paragraph position="23"> John fears failure and his brother does too.</Paragraph> <Paragraph position="24"> Assuming that John fears his own failure, then John's brother may either fear John's failure or his own failure. Our algorithm readily handles these cases with the appropriate role linking of implicit arguments.</Paragraph> <Paragraph position="25"> Not included in the class of referential elements are empty pronouns within infinitival clauses; these do not give rise to strict readings. For example, in (81) (81) John wants to leave, and Bill does too.</Paragraph> <Paragraph position="26"> there is only a sloppy reading for the target; it cannot be taken to mean that Bill wants John to leave. In our algorithm, the representation for empty pronouns will always have to be copied.</Paragraph> </Section> class="xml-element"></Paper>