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This paper defines a language L for specifying LFG grammars. This enables constraints on LFG's composite ontology (c-structures synchronised with f-structures) to be stated directly; no appeal to the LFG construction algorithm is needed. We use L to specify schemata annotated rules and the LFG uniqueness, completeness and coherence principles. Broader issues raised by this work are noted and discussed.
We provide a unified account of sentence-level and text-level anaphora within the framework of a dependency-based grammar model. Criteria for anaphora resolution within sentence boundaries rephrase major concepts from GB's binding theory, while those for text-level anaphora incorporate an adapted version of a Grosz-Sidner-style focus model.
In this paper we present a semantic study of motion complexes (ie. of a motion verb followed by a spatial preposition). We focus on the spatial and the temporal intrinsic semantic properties of the motion verbs, on the one hand, and of the spatial prepositions, on the other hand. Then, we address the problem of combining these basic semantics in order to formally and automatically derive the spatiotemporal semantics of a motion complex from the spatiotemporal properties of its components.
We present a new method for characterizing the interpretive possibilities generated by elliptical constructions in natural language. Unlike previous analyses, which postulate ambiguity of interpretation or derivation in the full clause source of the ellipsis, our analysis requires no such hidden ambiguity. Further, the analysis follows relatively directly from an abstract statement of the ellipsis interpretation problem. It predicts correctly a wide range of interactions between ellipsis and other semantic phenomena such as quantifier scope and bound anaphora. Finally, although the analysis itself is stated nonprocedurally, it admits of a direct computational method for generating interpretations.
Nominalization is a highly productive phenomena in most languages. The process of nominalization ejects a verb from its syntactic role into a nominal position. The original verb is often replaced by a semantically emptied support verb (e.g., "make a proposal"). The choice of a support verb for a given nominalization is unpredictable, causing a problem for language learners as well as for natural language processing systems. We present here a method of discovering support verbs from an untagged corpus via low-level syntactic processing and comparison of arguments attached to verbal forms and potential nominalized forms. The result of the process is a list of potential support verbs for the nominalized form of a given predicate.
An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have shown that the new models improve model performance.
Why should computers interpret language incrementally? In recent years psycholinguistic evidence for incremental interpretation has become more and more compelling, suggesting that humans perform semantic interpretation before constituent boundaries, possibly word by word. However, possible computational applications have received less attention. In this paper we consider various potential applications, in particular graphical interaction and dialogue. We then review the theoretical and computational tools available for mapping from fragments of sentences to fully scoped semantic representations. Finally, we tease apart the relationship between dynamic semantics and incremental interpretation.
Despite the large amount of theoretical work done on non-constituent coordination during the last two decades, many computational systems still treat coordination using adapted parsing strategies, in a similar fashion to the SYSCONJ system developed for ATNs. This paper reviews the theoretical literature, and shows why many of the theoretical accounts actually have worse coverage than accounts based on processing. Finally, it shows how processing accounts can be described formally and declaratively in terms of Dynamic Grammars.
The paper describes a parser for Categorial Grammar which provides fully word by word incremental interpretation. The parser does not require fragments of sentences to form constituents, and thereby avoids problems of spurious ambiguity. The paper includes a brief discussion of the relationship between basic Categorial Grammar and other formalisms such as HPSG, Dependency Grammar and the Lambek Calculus. It also includes a discussion of some of the issues which arise when parsing lexicalised grammars, and the possibilities for using statistical techniques for tuning to particular languages.
This paper is an introduction to natural language interfaces to databases (NLIDBs). A brief overview of the history of NLIDBs is first given. Some advantages and disadvantages of NLIDBs are then discussed, comparing NLIDBs to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems NLIDBs have to confront follows, for the benefit of readers less familiar with computational linguistics. The discussion then moves on to NLIDB architectures, portability issues, restricted natural language input systems (including menu-based NLIDBs), and NLIDBs with reasoning capabilities. Some less explored areas of NLIDB research are then presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal NLIDBs. The paper ends with reflections on the current state of the art.
In dialogues in which both agents are autonomous, each agent deliberates whether to accept or reject the contributions of the current speaker. A speaker cannot simply assume that a proposal or an assertion will be accepted. However, an examination of a corpus of naturally-occurring problem-solving dialogues shows that agents often do not explicitly indicate acceptance or rejection. Rather the speaker must infer whether the hearer understands and accepts the current contribution based on indirect evidence provided by the hearer's next dialogue contribution. In this paper, I propose a model of the role of informationally redundant utterances in providing evidence to support inferences about mutual understanding and acceptance. The model (1) requires a theory of mutual belief that supports mutual beliefs of various strengths; (2) explains the function of a class of informationally redundant utterances that cannot be explained by other accounts; and (3) contributes to a theory of dialogue by showing how mutual beliefs can be inferred in the absence of the master-slave assumption.
A discourse strategy is a strategy for communicating with another agent. Designing effective dialogue systems requires designing agents that can choose among discourse strategies. We claim that the design of effective strategies must take cognitive factors into account, propose a new method for testing the hypothesized factors, and present experimental results on an effective strategy for supporting deliberation. The proposed method of computational dialogue simulation provides a new empirical basis for computational linguistics.
This paper presents relevant issues that have been considered in the design of a general purpose lemmatizer/tagger for Basque (EUSLEM). The lemmatizer/tagger is conceived as a basic tool necessary for other linguistic applications. It uses the lexical data base and the morphological analyzer previously developed and implemented. Due to the characteristics of the language, the tagset here proposed in structured in for levels, so that each level is a refinement of the previous one in the sense that it adds more detailed information. We will focus on the problems found in designing this tagset and on the strategies for morphological disambiguation that will be used.
We prove a theorem stating that any semantics can be encoded as a compositional semantics, which means that, essentially, the standard definition of compositionality is formally vacuous. We then show that when compositional semantics is required to be "systematic" (that is, the meaning function cannot be arbitrary, but must belong to some class), it is possible to distinguish between compositional and non-compositional semantics. As a result, we believe that the paper clarifies the concept of compositionality and opens a possibility of making systematic formal comparisons of different systems of grammars.
A comparison was made of vectors derived by using ordinary co-occurrence statistics from large text corpora and of vectors derived by measuring the inter-word distances in dictionary definitions. The precision of word sense disambiguation by using co-occurrence vectors from the 1987 Wall Street Journal (20M total words) was higher than that by using distance vectors from the Collins English Dictionary (60K head words + 1.6M definition words). However, other experimental results suggest that distance vectors contain some different semantic information from co-occurrence vectors.
This paper shows first the problems raised by proper names in natural language processing. Second, it introduces the knowledge representation structure we use based on conceptual graphs. Then it explains the techniques which are used to process known and unknown proper names. At last, it gives the performance of the system and the further works we intend to deal with.
An experiment designed to explore the relationship between tagging accuracy and the nature of the tagset is described, using corpora in English, French and Swedish. In particular, the question of internal versus external criteria for tagset design is considered, with the general conclusion that external (linguistic) criteria should be followed. Some problems associated with tagging unknown words in inflected languages are briefly considered.
This paper presents a computational model of how conversational participants collaborate in order to make a referring action successful. The model is based on the view of language as goal-directed behavior. We propose that the content of a referring expression can be accounted for by the planning paradigm. Not only does this approach allow the processes of building referring expressions and identifying their referents to be captured by plan construction and plan inference, it also allows us to account for how participants clarify a referring expression by using meta-actions that reason about and manipulate the plan derivation that corresponds to the referring expression. To account for how clarification goals arise and how inferred clarification plans affect the agent, we propose that the agents are in a certain state of mind, and that this state includes an intention to achieve the goal of referring and a plan that the agents are currently considering. It is this mental state that sanctions the adoption of goals and the acceptance of inferred plans, and so acts as a link between understanding and generation.