Type-driven suppression of redundancy in the generation of inference-rich reports

McDonald, David D.

doi:10.1007/3-540-55399-1_6

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Our characterization of sentence planning is not so far from that of Appelt (1985), McDonald (1992), and Thomason and colleagues (Thomason, Hobbs, and Moore 1996;Thomason and Hobbs 1997). All these researchers propose to use representations of interpretation to solve microplanning problems flexibly and interactively.…”

Section: Previous Workmentioning

confidence: 65%

Microplanning with Communicative Intentions: The SPUD System

Stone

Doran

Webber

et al. 2003

Computational Intelligence

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The process of microplanning in natural language generation (NLG) encompasses a range of problems in which a generator must bridge underlying domain‐specific representations and general linguistic representations. These problems include constructing linguistic referring expressions to identify domain objects, selecting lexical items to express domain concepts, and using complex linguistic constructions to concisely convey related domain facts. In this paper, we argue that such problems are best solved through a uniform, comprehensive, declarative process. In our approach, the generator directly explores a search space for utterances described by a linguistic grammar. At each stage of search, the generator uses a model of interpretation, which characterizes the potential links between the utterance and the domain and context, to assess its progress in conveying domain‐specific representations. We further address the challenges for implementation and knowledge representation in this approach. We show how to implement this approach effectively by using the lexicalized tree‐adjoining grammar (LTAG) formalism to connect structure to meaning and using modal logic programming to connect meaning to context. We articulate a detailed methodology for designing grammatical and conceptual resources which the generator can use to achieve desired microplanning behavior in a specified domain. In describing our approach to microplanning, we emphasize that we are in fact realizing a deliberative process of goal‐directed activity. As we formulate it, interpretation offers a declarative representation of a generator's communicative intent. It associates the concrete linguistic structure planned by the generator with inferences that show how the meaning of that structure communicates needed information about some application domain in the current discourse context. Thus, interpretations are plans that the microplanner constructs and outputs. At the same time, communicative intent representations provide a rich and uniform resource for the process of NLG. Using representations of communicative intent, a generator can augment the syntax, semantics, and pragmatics of an incomplete sentence simultaneously, and can work incrementally toward solutions for the various problems of microplanning.

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Section: Previous Workmentioning

confidence: 65%

Microplanning with Communicative Intentions: The SPUD System

Stone

Doran

Webber

et al. 2003

Computational Intelligence

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“…The rhetorical connection between a sentence and surrounding discourse should also be described with adjuncts (Huang, 1994). Adjuncts giving details about an event should be included only after reasoning that these adjuncts are in fact necessary in context (McDonald, 1992).…”

Section: Discussionmentioning

confidence: 99%

Paying Heed to Collocations

Stone¹,

Doran²

1996

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In this paper, we introduce a system, Sentence Planning Using Description, which generates collocations within the paradigm of sentence planning. SPUD simultaneously constructs the semantics and syntax 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 and lexical interactions between multiple descriptions in a sentence, At the same time, it exploits linguistically motivated, declarative specifications of the discourse functions of syntactic constructions to make contextually appropriate syntactic choices.

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“…McDonald (1983) uses something he calls grammatically driven ellipsis to reduce redundant verbs and merge relations with common arguments. McDonald (1992) discussed the problem of suppressing redundancy (which we call aggregation) in inference-rich texts, as well as architectural issues in the generation problem. The problem of suppressing redundancy in texts is closely related to what to say and also in which order to say it.…”

Section: Previous Researchmentioning

confidence: 99%

Aggregation in Natural Language Generation

Dalianis

1999

Computational Intelligence

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The content of real-world databases, knowledge bases, database models, and formal specifications is often highly redundant and needs to be aggregated before these representations can be successfully paraphrased into natural language. To generate natural language from these representations, a number of processes must be carried out, one of which is sentence planning where the task of aggregation is carried out. Aggregation, which has been called ellipsis or coordination in Linguistics, is the process that removes redundancies during generation of a natural language discourse, without losing any information.The article describes a set of corpus studies that focus on aggregation, provides a set of aggregation rules, and finally, shows how these rules are implemented in a couple of prototype systems. We develop further the concept of aggregation and discuss it in connection with the growing literature on the subject. This work offers a new tool for the sentence planning phase of natural language generation systems.

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Type-driven suppression of redundancy in the generation of inference-rich reports

Cited by 5 publications

References 11 publications

Microplanning with Communicative Intentions: The SPUD System

Microplanning with Communicative Intentions: The SPUD System

Paying Heed to Collocations

Aggregation in Natural Language Generation

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