Anaphoric Cues for Coherence Relations - CiteSeerX

Anaphoric Cues for Coherence Relations Holger Schauer and Udo Hahn Text Understanding Lab Freiburg University, Germany http://www.coling.uni-freiburg.de

Abstract In this paper, we focus on the way how the rhetorical discourse structure of texts, in terms of coherence relations, and different types of referring expressions depend on each other. In particular, we concentrate on the impact the resolution of anaphora has on the derivation of coherence relations. We introduce an algorithm which combines such analyses and provide a preliminary evaluation of its empirical validity.

1

Introduction

Models of discourse structure typically distinguish between the micro level of cohesion and the macro level of coherence. A common means to establish cohesion is by way of coreference (Halliday & Hasan 76). In Example (1-b), the pronominal “it” refers to “the Vaio F190”, just like the definite noun phrase “the notebook” or the bridging anaphora “the batteries” [of “the Vaio F190” ].1 Such reference relations impose accessibility constraints on the resolution of subsequent anaphora, e.g., “weight” in (1-c) should no longer be accessible after (1-d) has been processed. However, the referential relationships between objects are usually semantically poor, i.e., finding the antecedent for an anaphoric expression adds few, if any significant semantic information to the analysis.

The Vaio F190
 offers fine equipment. It
  comes with a DVD-ROM drive and a 6 GB harddisk. c. Still the notebook
   has a weight of only 6 lbs. d. Its
  14”-display shows very bright colors with clear contrasts. e. The batteries
  last two hours.

(1) a. b.

In contrast, work on the coherence of texts is concerned with semantically rich relations among discourse entities as evidenced by coherence relations like Cause or Evaluation.2 For instance, (1-a) might 1 The referee is marked by the lower index, while the type of referring expression is indicated by the upper index, where  stands for pronominal anaphora,  for nominal anaphora and  for bridging anaphora. 2 We draw on the repertoire of coherence relations as proposed in the framework of Rhetorical Structure Theory (Mann &

be taken to Evaluate (1-b). Rhetorical Structure Theory [RST] (Mann & Thompson 88) distinguishes between relations that relate equally important units and those that relate a more important (nucleus) and a less important unit (satellite). Such structural prescriptions lead naturally to a tree-like analysis of a text’s rhetorical discourse structure. Recent research on computing coherence relations relies very much on so-called discourse cues, i.e., words or phrases explicitly hinting at a particular discourse relation (Knott & Dale 94; Marcu 97). The conjunction “still”, e.g., in Example (1-c) can be taken to indicate a Concession relation between (1-b) and (1-c). In our work, however, we found that discourse cues might not always be sufficient. Moreover, while cue phrases may signal which coherence relation is appropriate, they usually do not determine the target unit to which a new unit should be attached. Therefore, it may be rewarding to consider the possible contribution of further cohesion devices to a text’s coherence structure. While referring expressions are generally seen as such a cohesive device, there is only little work on how coreference relations might influence the creation of the discourse structure in terms of coherence relations. We address exactly this point.

2 2.1

Empirical Considerations Reconsidering the Relevance of Cue Phrases

In a first round of experiments, we tested whether cue phrases are a reliable indicator for the presence of coherence relations, as argued for by (Knott & Dale 94; Marcu 97). We examined 37 texts, taken from a corpus of German-language information technology test reports. This gave us a total of about 6,850 text tokens, which we annotated manually using a version of RSTT OOL (Marcu et al. 99) that we had previously augmented with methods for the annotation of cue phrases and coreferences. The RST analyses were performed by the first author and one student, based on the considerations from Thompson 88). These relations appear emphasized and Capitalized.

Section 3. In case of annotation conflicts, the two coders discussed and then consensually merged divergent RST annotations. Finally, we ended up with 609 elementary units and 408 complex units, all related by 549 coherence relations. Next, we determined the number of cue phrases applying an intentionally benevolent policy. Whenever we found that the assignment of some coherence relation was probably due to such a phrase, we counted it as justified by a cue phrase. For the 549 relations, a total of 118 cue phrases were identified on that assumption. Still, some relations such as Joint provide only weak semantic contributions and are never explicitly marked by cue phrases. Hence, 95 occurrences of such relations were also not taken into consideration. This leaves us with  (61.2%) semantically rich coherence relations that remain lexically unmarked. At least for our text sample, cue phrases do not seem to constitute a sufficiently reliable indicator of a text’s discourse structure. So, additional criteria for identifying coherence relations need to be considered. 2.2

Interaction Patterns of Reference and Coherence Relations

Referential relations between nominal expressions can be established by different means, such as pronouns (including relative pronouns), proper names or definite noun phrases, all of which rely on some relation of object identity. A complementary phenomenon are bridging anaphora (Hahn et al. 96), as illustrated by “the batteries” [of the Vaio F190 ] in Example (1), which establish a particular conceptual relation between the discourse entities involved. Also important, in our sample at least, are set-member (sm) relationships. In Example (2), the different monitor variants are all members of the set ‘three new monitor models’. (2) a. Panasonic presented three new monitor models
 . b. The LC50
 is the low-budget model of the new 15”-series. c. The LC50S
 has additional speakers and an USB-connection. d. Dressed in black, the LC50SG
 is the bestlooking model. These phenomena may even interact. A definite noun phrase can be a bridging anaphora to an entity just mentioned, but at the same time it can be objectidentical to a discourse entity mentioned earlier. We annotated all referential relations in such cases. Given that an entity is referred to more than once in a text, which expression should be marked as coref-

erential? Does, e.g., the nominal anaphor “the notebook” in (1-c) refer to the pronominal “it” in (1-b), or directly to “the Vaio F190” in (1-a)? (Cristea et al. 99) argue that the referring expressions in a text can be seen as forming an equivalence class of expressions that all make reference to the same entity. For our purposes, we concentrate on two distinguished members of this class: If more than one antecedent was available for a referring expression, we marked the antecedent that was closest in terms of textual proximity (“it” in (1-b)), and also the one that was closest if one would follow a path along the discourse structure (“The Vaio F190” in (1-a); cf. also Figure 1).

Antecedent

Anaphoric expression

Anaphoric expression

Referential chain with regard to textual adjacency Closest antecedent wrt. rhetorical discourse structure (Unspecified) RST−Relation

Figure 1: Direct Antecedents and RST This leaves us with the question under which conditions a referential link between two expressions should be counted as justifying the structural configuration of a coherence relation. After all, it is not sufficient to count how many units containing coreferential expressions can be found. According to RST, if a text is coherent, it can be analyzed completely in a tree that spans the entire text. Now, two units containing coreferential expressions are obviously always connected by some path from one unit to the other along the coherence relations. It is, hence, necessary to consider whether the relationship between two discourse units is really due to coreference or to other factors. “Other factors” in our evaluation boil down to considering merely cue phrases. If one of the coreferential units also contained a cue phrase, we replaced the anaphoric expression with a non-coreferential expression. If this would make a different unit to connect to more plausible, then the cue phrase is likely to justify the coherence relation, not the coreference relation. As a second criterion, we checked whether the elimination of the cue phrase leads to the same structural configuration or requires subsequent changes. If the configuration of the discourse structure has to be

changed, again the coreferential relation does not affect the discourse structure under scrutiny. It has also been claimed that the referential structure of a text and its discourse structure, in terms of coherence relations, interact in such a way that anaphora resolution should obey the so-called right frontier principle (Webber 91). This means that anaphora may refer only to entities that are accessible at nodes of the right frontier of the discourse structure tree. For the right frontier principle to be applicable, it is necessary to promote antecedent candidates of nuclei upward to the group node that spans the complex unit formed by the coherence relation and its units in order to be accessible at all. 2.3

Data on Interactions of Reference and Coherence Relations

For the set of 37 texts, we identified a total of 494 referential relations, which were categorized as 329 cases of object-identity relations, 103 bridging anaphors and 62 set-member relations. The 494 referring expressions relate 386 out of 609 (elementary) discourse units (63.4%). This number is lower than the one of referential expressions, because sometimes a unit carries more than one referring expression. From the 386 coreferential units, only 129 (33.4%) are structurally adjacent, i.e., the anaphoric unit bearing the referring expression is directly connected (path length=1) to the antecedent unit (cf. Figure 1). This means that traversing the paths between the remaining 257 coreferential units involves looking for antecedents in group nodes, according to the right frontier principle. Interestingly enough, from these 257 cases, 67 coreferential units are only reachable if one violates the right frontier principle, i.e., the antecedent is not part of a common parent group node. Considering the 386 coreferential units and 118 cue phrases, 53 units which contained a referring expression also contained a cue phrase. In 33 of these cases, the complex units that can be justified by the occurrence of a cue phrase are formed by the clauses of single sentences. In Example (3-b), the cue “although” and the syntactic construction establish a local Concession relation. (3) a. The LC90S
 by Panasonic is a 19”- display. b. Although its
  screen size of 482 mm corresponds to a conventional 21”-monitor, c. considerably less space is required. In such a case, the cue phrase is stronger than the referential link so that the cue phrase has to be accounted for first, with subsequent consideration of the

referential links, i.e., linking the entire sentence ((3-b) and (3-c)) to (3-a). 65 units (10.7%) bear only cue phrases, and 159 units (26.1%) carry neither coreference nor cue phrase indications and, hence, seem to require a lot of world knowledge and heavy inferencing. Summing up, [386-33 =] 353 units (58.0% of the elementary units) contain coreferential expressions that justify the selection of the units of coherence relations.

3 3.1

Discourse Structure Analysis RST Revisited

RST focuses on a ‘pre-realizational’ structure, i.e., it is not primarily concerned with concrete, surfacebound text phenomena. So it is often difficult to judge the appropriateness of a discourse structure as the author’s intention is rarely fully revealed, neither to human annotators nor to NLP systems.3 This is especially true for judging which aspect of an utterance is considered by the author to be most crucial, though such an interpretation is at the heart of the theory. Since RST does not account for any explicit cue from which the rhetorical structure of the discourse may be derived, referential constraints like the dependency of an anaphoric expression on its antecedent are disregarded, too.4 While this is not an issue for RST as a theory per se, it is a prerequisite for any system that must compute a text’s discourse structure. Consider, e.g., the following fragment repeated from Example (1): (4) a. b.

The Vaio F190
 offers fine equipment. It
  comes with a DVD-ROM and a 6 GB harddisk.

In classical RST, example (4-a) may stand in an Evaluation relation to (4-b). The definition of Evaluation requires that the satellite evaluate the nucleus (cf. Figure 2).5 However, this does not capture an obvious case of structural dependency, viz. that the pronominal anapher “it” cannot be interpreted correctly without the antecedent — so (4-b) depends on (4-a). In RST, this referential dependency can be accounted for by analyzing (4-b) as giving Evidence 3 (Mann & Thompson 88, p. 246) already acknowledge: “During analysis, judgments must be made about the writer or readers. Since such judgments cannot be certain, they must be plausibility judgments.” 4 The same observation holds for syntactic constraints. As (Webber et al. 99) point out, syntactic constraints that are associated with cue phrases also influence a text’s discourse structure. 5 The depicted structures reflect standard RST schemata. The arrow points at the nucleus.

When more than one coreference relation is involved, things become more complex. Consider the following example:

Evaluation

It

Vaio F190

Figure 2: Structure of Evaluation for (4-a) (cf. Figure 3). An Evidence relation applies when the hypothesis is the most important part, i.e., the nucleus. Such an analysis, however, neglects the attribute “fine” in (4-a), which is quite an explicit evaluation. Evidence

Vaio F190

It

Figure 3: Structure of Evidence We therefore propose to change the focus of RSTstyle theories and to incorporate such structural constraints within the representation of a text’s discourse structure. In Example (4), we introduce an Evaluation-N(ucleus) relation which reflects that the nucleus (4-a) evaluates the satellite (4-b). This proposal covers the explicit structural dependency and allows an analysis in terms of a relation which we consider most adequate.

(5) a. The LC90S
 by Panasonic is a 19”-display. b. Although its
   screen size of 482 mm corresponds to a conventional 21”-monitor, c. considerably less space is required. d. The device
  can be attached to a videocard via an USB-connector. Obviously, the nominal anaphor “the device” in (5-d) requires an antecedent. One reasonable choice could be the pronominal “its” in (5-b), leading to a resolution in terms of “the LC90S”. However, this is not reflected in the discourse structure that seems most appropriate (cf. Figure 5). The topic of (5-b) and (5-c) (the “size” ) is not further elaborated on in (5-d), so one might say that a mini-segment boundary between these two sentences is encountered. Hence, it would also be incorrect to analyze (5-d) as an Elaboration of (5a-c), because (5-d) elaborates only (5-a). Elaboration Elaboration 5a

Concession

5b

5d

5c

Evaluation−N

Figure 5: RST Analyses for Example (5) Vaio F190

It

Figure 4: Structure of Evaluation-N 3.2

Combining Coreference and Coherence Relations within RST

In the annotated text sample, we found that when two elementary discourse units contain coreferring expressions, either these elementary units or the complex discourse unit they participate in are usually connected by a coherence relation which subordinates the anaphoric unit. In the simplest case, this may be an Elaboration relation, but further linguistic cues or inferences might give rise to semantically “richer” relations.6 As we are mostly concerned with structural configurations, accounting for these additional inferences will not be an issue here (Schauer & Hahn 00). 6 Section 5 shows that these cues or inferences are sometimes strong enough to override the basic structural configuration, resulting in a multi-nuclear configuration or even a subordination of the unit which contains the antecedent.

Example (6) illustrates a preference for local attachment. (6) a. Apple presented a new PDA, the MessagePad 130
 . b. The new display
    has a surface that is more durable than in prior versions. c. The background lighting
  can be controlled with a switch. In (6-c) “the background lighting” is a bridging anaphor to the “the new display” which is, in turn, a bridging anaphor to the “MessagePad 130”. “The new display”, however, introduces a new discourse entity that is distinct from, though part of, the “MessagePad”. So, unit (6-c) is subordinated to (6-b) (cf. Figure 6), because the referential dependency of “the background lighting” should be resolved as locally as possible. In contrast, in Example (5) there is no such mediated dependency between the anaphoric expressions in (5-d) and (5-b).

Elaboration

6a

Elaboration

6b

6c

Figure 6: RST Analyses for Example (6)

Summing up, the structural configurations that we typically found illustrate a preference to connect a new unit to the highest node (Example (5)) that provides direct antecedents for the referring expression in the new unit (Example (6)).

4

From Coreferences to Coherence

The configurations described in the previous section naturally lead to a combined account of deriving a text’s rhetorical discourse structure and resolving its referring expressions. Basically, the algorithm we propose exploits the successful resolution of anaphoric expressions in order to determine the target discourse unit to which a new unit should be connected. This, in turn, restricts the set of units which must be searched for resolving further referring expressions. The algorithm (cf. Figure 7) requires several capabilities for anaphora resolution. First, for a given unit candidate (a clause) a set of noun phrases need to be identified that may be anaphoric expressions (anaphoric expressions), basically pronouns and definite noun phrases. Second, some resolution process (match(ana cand,ante list)) is necessary in order to check whether an anaphoric expression can be resolved considering a given list of possible antecedents. Third, while not essential for the algorithm, we assume some ordering on the antecedent lists (centers forward(clause)) in line with, say, centering theory (Grosz et al. 95). The basic data structure to operate on is a node of a tree. Leaf nodes represent elementary discourse units. Their data field holds a list of nominal expressions that are accessible for coreference resolution at that node. The algorithm loops through all clauses of a text, building up both the discourse tree and the antecedent lists incrementally. Basically, whenever a new clause is considered, the right frontier of the tree is checked for plausible antecedents. First, the lowest rightmost node is checked (lowest right node(tree))

for possible antecedents. If there is none, its predecessor on that rightmost branch of the tree is checked (predecessor(node)). In order to determine the target node to which the new unit should be connected to, one has to find all anaphoric expressions (ana cand), i.e., all nodes on the right frontier that contain an antecedent for the anaphoric expressions in the new unit. When all antecedent nodes are determined, the highest node that provides antecedents for all resolvable anaphoric expressions in the new unit is taken as the target node (find highest nodematching all), in accordance with the discussion in the previous section. If a new unit contains no referential expression, then the algorithm makes no prediction. If the node to connect to has been found, the new unit is connected to it, i.e., the new unit is established as a satellite to the target unit. This way, the new unit opens a new right-most branch and, hence, becomes the lowestright node of the tree. So, the new right frontier consists of the newly attached unit (with its antecedent list), the modified node (still bearing the same antecedent list) and its predecessors.

5

Evaluation of the Algorithm

In Section 2.1 we already mentioned that 37 German technical reports were segmented into 609 elementary units, leading to 549 relations and 408 complex units. We evaluated the performance of the implemented algorithm from Figure 7 by judging — based on our intuition — in how many cases the algorithm predicted a structure that matched the one we felt most appropriate, and in how many cases the predictions seemed implausible. Obviously, when a new unit contains no coreferring expression, the algorithm makes no prediction. This is the case for 145 units out of 609 new units (23.8%). 65 of those units contained cue phrases, while the remaining cases seem to require rather complex inferences to compute the target unit and their relation. The algorithm determined a node to connect a new unit to in 464 cases (out of 609 elementary units). This number is smaller than the number of coreferring expressions because the new unit that needs to be connected can contain more than one coreferring expression. In 297 cases (64.0%) the prediction was correct, i.e., the predicted target node was the one we also found most appropriate in the manual annotations. In 167 cases (36.0%), however, the predictions seemed implausible. From these cases, 81 were due

t r e e : = t r e e ( c e n t e r s f o r w a r d ( f i r s t ( c l a u s e s ) ) , NIL ) clauses : = rest ( clauses ) f o r a l l c l a u s e : = c l a u s e s do a n a n o d e s : = a r r a y of l i s t s of nodes . f o r a l l a n a c a n d : = a n a p h o r i c e x p r e s s i o n s ( c l a u s e ) do node : = l o w e s t r i g h t n o d e ( t r e e ) w h ile node do i f match ( a n a c a n d , a n t e l i s t ( node ) ) then a n a n o d e s [ a n a c a n d ] : = append ( a n a n o d e s [ a n a c a n d ] , node ) fi node : = p r e d e c e s s o r ( node ) done done t ar get node : = f i n d h i g h e s t n o d e m a t c h i n g a l l ( ana nodes ) /  found a t l e a s t one a n t e c e d e n t node  / i f t a r g e t n o d e then /  c o n n e c t new u n i t t o old node  / c o n n e c t ( t a r g e t n o d e , t r e e ( c e n t e r s f o r w a r d ( c l a u s e ) , NIL ) ) fi done Figure 7: Algorithm for the Integrated Computation of Coreferences and Coherence Structure to cue phrases. Interestingly, 75 out of these 81 are intra-sentential cases, i.e., the related units are clauses within a single sentence (cf. Example (5-b) and (5-c)). No wonder then that such intra-sentential phenomena need to be accounted for prior to dealing with coreferences. When one first accounts for intra-sentential discourse structure and only then considers the coreference relations, the prediction of the target unit to which this complex unit should be related is correct in 62 out of these 75 cases. Thereby the algorithm would correctly predict [464+62 =] 526 cases (86.4%). We cautiously interpret these results in two ways: Either, it can be taken as a hint that the minimal size of discourse units should be the entire sentence. Or, it may indicate that intra-sentential coherence relations should not be seen as restricting the availability of expressions for further anaphora resolution.

6

Discussion of Related Work

(Vonk et al. 92) argue that overly specific definite noun phrases may signal segment boundaries. Their data also suggests that pronouns tend to be used when other means to signal thematic shifts are available. The evidence we found and the evaluation of our algorithm indicate that such thematic shifts occur quite frequently without overly specific noun phrases. (Corston-Oliver 98) modifies the cue phrase approach of (Marcu 97) similar to us. However, he does not consider how several coreference relations interact with the resulting discourse structure. Also, it re-

mains unclear how the correct target node to attach to is identified in his approach. Segmented Discourse Representation Theory (Asher 93) provides a framework for dealing with discourse structure which incorporates referential accessibility constraints, too. Asher, however, does not rely on coreferences for establishing target units. Rather the derivation of a coherence relation (and thereby of the target unit to connect a new unit to) relies on quite abstract connections between “events”. While recognizing coreference relations certainly requires domain knowledge and inference capabilities, too, recognizing connections between events seems a very hard task. In contrast, our approach is more light-weight by design, though more feasible. (Webber 91) argues for the resolution of discoursedeictic references along the right frontier of a discourse structure. In order to establish that structure two basic operations on trees are proposed. Webber, though, makes no clear commitment when to choose which operation and how the resolution of references influences the tree’s construction. (Webber et al. 99) apply the same basic two operations for incorporating cue phrases in a TAG-driven approach to discourse structure. Our algorithm may well be integrated with their approach, e.g., to account for the cases of intrasentential phenomena discussed in the evaluation section. Our approach, however, is incompatible with Webber’s with respect to a single though crucial point: The operations from (Webber 91) cannot account for

the structures discussed for Example (5) in Section 3.2. After adjoining (5b-c) to the root of (5), one can attach or adjoin (5-d) only to (5b-c) or to (5a-c). This reflects, however, a continuation of either segment, against which we have argued — (5-d) elaborates only on (5-a). The work of (Cristea et al. 99) on coreference and discourse structure, though based on the original RST model, has already revealed that the resolution of referential expressions can considerably benefit from following a text’s discourse structure. As the proposed modifications to RST exactly aim at referential dependencies, we expect to earn even higher benefits for anaphora resolution with analyses made along the line of our approach.

7

Conclusion

Coherence relations, though sometimes signalled by explicit cue words (Knott & Dale 94; Marcu 97), are basically a representational construct at the level of conceptual text representation. In order to cope with those cases which are marked by referential constraints, we focused on the role of anaphoric expressions, basically within the framework of RST (Mann & Thompson 88). We set out to overcome its lack of capturing referential dependencies and worked out an approach in which the automatic derivation of a text’s discourse structure is made dependent on the resolution of coreferring expressions. We presented an algorithm which computes the discourse structure of a text by using the successful resolution of referring expressions as an indicator for determining the target units of coherence relations to which new units should be connected. Our evaluation yielded plausible predictions of a text’s discourse structure in 64% of the cases. The algorithm must be extended to handle cue phrases as well. To realize this extension, the algorithm just has to be delayed until the (intra-sentential) cue phrases have been accounted for. Such an extension would increase the level of plausible predictions of discourse structure up to 86%. The combined algorithm has been implemented in the text understanding system S YN D I KAT E (Hahn & Romacker 00) which already provides means for handling referential relations of object-identity and bridging anaphora (Hahn et al. 96).

8

Acknowledgments

H. Schauer was supported by a grant from DFG within the Graduate Programme Human and Machine Intelligence.

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