We present an algorithm for simultaneously constructing both the syntax and semantics of a sentence using a Lexicalized Tree Adjoining Grammar (LTAG). This approach captures naturally and elegantly the interaction between pragmatic and syntactic constraints on descriptions in a sentence, and the inferential interactions between multiple descriptions in a sentence. At the same

Centering was formulated as a model of the relationship between attentional state, the form of referring expressions, and the coherence of an utterance within a discourse segment (Grosz, Joshi and Weinstein, 1986; Grosz, Joshi and Weinstein, 1995). In this chapter, I argue that the restriction of centering to operating within a discourse segment should be abandoned in order to integrate centering with a model of global discourse structure. The within-segment restriction causes three problems. The first problem is that centers are often continued over discourse segment boundaries with pronominal referring expressions whose form is identical to those that occur within a discourse segment. The second problem is that recent work has shown that listeners perceive segment boundaries at various levels of granularity. If centering models a universal processing phenomenon, it is implausible that each listener is using a different centering algorithm.The third issue is that even for utterances within a discourse segment, there are strong contrasts between utterances whose adjacent utterance within a segment is hierarchically recent and those whose adjacent utterance within a segment is linearly recent. This chapter argues that these problems can be eliminated by replacing Grosz and Sidner's stack model of attentional state with an alternate model, the cache model. I show how the cache model is easily integrated with the centering algorithm, and provide several types of data from naturally occurring discourses that support the proposed integrated model. Future work should provide additional support for these claims with an examination of a larger corpus of naturally occurring discourses.

Centering Theory is the best known framework for theorizing about local coherence and salience; however, its claims are articulated in terms of notions which are only partially specified, such as ‘utterance’, ‘realization’, or ‘ranking’. A great deal of research has attempted to arrive at more detailed specifications of these PARAMETERS of the theory; as a result, the claims of Centering can be INSTANTIATED in many different ways. We investigated in a systematic fashion the effect of these different ways of setting the parameters on the theory’s claims. Doing this required, first of all, to clarify what the theory’s claims are (one of our conclusions being that what has become known as ’Constraint 1 ’ is actually a central claim of the theory). Secondly, we had to clearly identify these parametric aspects: e.g., we argue that the notion of ‘pronoun ’ used in Rule 1 should be considered a parameter. Thirdly, we had to find appropriate methods for evaluating these claims. We found that while the theory’s main claim about salience and pronominalization, Rule 1–a preference for pronominalizing the CB–is verified with most instantiations, Constraint 1–a claim about (entity) coherence and CB uniqueness–is much more instantiation-dependent: it is not verified if the parameters are instantiated according to very mainstream views (‘Vanilla instantiation’), it only holds if indirect realization is allowed, and is violated by between 20

Entities Donna K. Byron Department of Computer Science P.O. Box 270226 Rochester, NY 14627 dbyron@cs.rochester.edu Abstract This paper describes PHORA, a technique for resolving pronominal reference to either indi- vidual or abstract entities. It defines processes for evoking abstract referents from discourse and for resolving both demonstrative and personal pronouns. It successfully interprets 72% of test pronouns, compared to 37% for a lead- ing technique without these features.

Natural language generation (NLG) is first and foremost a reasoning task. In this reasoning, a system plans a communicative act that will signal key facts about the domain to the hearer. In generating action descriptions, this reasoning draws on characterizations both of the causal properties of the domain and the states of knowledge of the participants in the conversation. This dissertation shows how such characterizations can be specified declaratively and accessed efficiently in NLG. The heart of this dissertation is a study of logical statements about knowledge and action in modal logic. By investigating the proof-theory of modal logic from a logic programming point of view, I show how many kinds of modal statements can be seen as straightforward instructions for computationally manageable search, just as Prolog clauses can. These modal statements provide sufficient expressive resources for an NLG system to represent the effects of actions in the world or to model an addressee whose knowledge in some respects exceeds and in other respects falls short of its own. To illustrate the use of such statements, I describe how the SPUD sentence planner exploits a modal knowledge base to

We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora and definite noun phrase coreference. Specifically, we compare an algorithm that relies on links encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora by means of shallow lexico-semantic patterns. As corpora we use the British National Corpus (BNC), as well as the Web, which has not been previously used for this task. Our results show that (a) the knowledge encoded in WordNet is often insufficient, especially for anaphor– antecedent relations that exploit subjective or context-dependent knowledge; (b) for otheranaphora, the Web-based method outperforms the WordNet-based method; (c) for definite NP coreference, the Web-based method yields results comparable to those obtained using WordNet over the whole data set and outperforms the WordNet-based method on subsets of the data set; (d) in both case studies, the BNC-based method is worse than the other methods because of data sparseness. Thus, in our studies, the Web-based method alleviated the lexical knowledge gap often encountered in anaphora resolution and handled examples with context-dependent relations between anaphor and antecedent. Because it is inexpensive and needs no hand-modeling of lexical knowledge, it is a promising knowledge source to integrate into anaphora resolution systems. 1.

SRI International I present a unified account of interpretation preferences of stressed and unstressed pronouns in discourse. The central intuition is the Complementary Preference Hypothesis that predicts the interpretation preference of a stressed pronoun from that of an unstressed pronoun in the same discourse position. The base preference must be computed in a total pragmatics module including commonsense preferences. The focus constraint in Rooth’s theory of semantic focus is interpreted to be the salient subset of the domain in the local attentional state in the discourse context independently motivated for other purposes in Centering Theory. 1

this article should be addressed to Dr. E. G. Bard, Department of Linguistics, University of Edinburgh, Adam Ferguson Building, George Square, Edinburgh EH8 9LL, U.K. E-mail: ellen@ling.ed.ac.uk

Multimodal conversational interfaces provide a natural means for users to communicate with computer systems through multiple modalities such as speech, gesture, and gaze. To build effective multimodal interfaces, understanding user multimodal inputs is important. Previous linguistic and cognitive studies indicate that user language behavior does not occur randomly, but rather follows certain linguistic and cognitive principles. Therefore, this paper investigates the use of linguistic theories in multimodal interpretation. In particular, we present a greedy algorithm that incorporates Conversation Implicature and Givenness Hierarchy for efficient multimodal reference resolution. Empirical studies indicate that this algorithm significantly reduces the complexity in multimodal reference resolution compared to a previous graph-matching approach. One major advantage of this greedy algorithm is that the prior linguistic and cognitive knowledge can be used to guide the search and significantly prune the search space. Because of its simplicity and generality, this approach has the potential to improve the robustness of interpretation and provide a more practical solution to multimodal input interpretation.

The definitions of the basic concepts, rules, and constraints of centering theory involve underspecified notions such as ‘previous utterance’, ‘realization’, and ‘ranking’. We attempted to find the best way of defining each such notion among those that can be annotated reliably, and using a corpus of texts in two domains of practical interest. Our main result is that trying to reduce the number of utterances without a backwardlooking center (CB) results in an increased number of cases in which some discourse entity, but not the CB, gets pronominalized, and viceversa. 1