Knowledge Acquisition for Content Selection

An important part of building a natural-language generation (NLG) system is knowledge acquisition, that is deciding on the specific schemas, plans, grammar rules, and so forth that should be used in the NLG system. We discuss some experiments we have performed with KA for content-selection rules, in the context of building an NLG system which generates health-related material. These experiments suggest that it is useful to supplement corpus analysis with KA techniques developed for building expert systems, such as structured group discussions and think-aloud protocols. They also raise the point that KA issues may influence architectural design issues, in particular the decision on whether a planning approach is used for content selection. We suspect that in some cases, KA may be easier if other constructive expert-system techniques (such as production rules, or case-based reasoning) are used to determine the content of a generated text.Comment: To appear in the 1997 European NLG workshop. 10 pages, postscrip

Floating constraints in lexical choice

Lexical choice is a computationally complex task, requiring a generation system to consider a potentially large number of mappings between concepts and words. Constraints that aid in determining which word is best come from a wide variety of sources, including syntax, semantics, pragmatics, the lexicon, and the underlying domain. Furthermore, in some situations, different constraints come into play early on, while in others, they apply much later. This makes it difficult to determine a systematic ordering in which to apply constraints. In this paper, we present a general approach to lexical choice that can handle multiple, interacting constraints. We focus on the problem of floating constraints, semantic or pragmatic constraints that float, appearing at a variety of different syntactic ranks, often merged with other semantic constraints. This means that multiple content units can be realized by a single surface element, and conversely, that a single content unit can be realized by a variety of surface elements. Our approach uses the Functional Unification Formalism (FUF) to represent a generation lexicon, allowing for declarative and compositional representation of individual constraints

Abstraction, Visualization, and Evolution of Process Models

The increasing adoption of process orientation in companies and organizations has resulted in large process model collections. Each process model of such a collection may comprise dozens or hundreds of elements and captures various perspectives of a business process, i.e., organizational, functional, control, resource, or data perspective. Domain experts having only limited process modeling knowledge, however, hardly comprehend such large and complex process models. Therefore, they demand for a customized (i.e., personalized) view on business processes enabling them to optimize and evolve process models effectively. This thesis contributes the proView framework to systematically create and update process views (i.e., abstractions) on process models and business processes respectively. More precisely, process views abstract large process models by hiding or combining process information. As a result, they provide an abstracted, but personalized representation of process information to domain experts. In particular, updates of a process view are supported, which are then propagated to the related process model as well as associated process views. Thereby, up-to-dateness and consistency of all process views defined on any process model can be always ensured. Finally, proView preserves the behaviour and correctness of a process model. Process abstractions realized by views are still not sufficient to assist domain experts in comprehending and evolving process models. Thus, additional process visualizations are introduced that provide text-based, form-based, and hierarchical representations of process models. Particularly, these process visualizations allow for view-based process abstractions and updates as well. Finally, process interaction concepts are introduced enabling domain experts to create and evolve process models on touch-enabled devices. This facilitates the documentation of process models in workshops or while interviewing process participants at their workplace. Altogether, proView enables domain experts to interact with large and complex process models as well as to evolve them over time, based on process model abstractions, additional process visualizations, and process interaction concepts. The framework is implemented in a proof-ofconcept prototype and validated through experiments and case studies

Practical Issues in Automatic Documentation Generation

PLANDoc, a system under joint development by Columbia and Bellcore, documents the activity of planning engineers as they study telephone routes. It takes as input a trace of the engineer's interaction with a network planning tool and produces 1-2 page summary. In this paper, we describe the user needs analysis we performed and how it influenced the devel- opment of PLANDoc. In particular, we show how it pinpointed the need for a sub-language specification, allowing us to identify input messages and to characterize the different sentence paraphrases for realizing them. We focus on the systematic use of conjunction in combination with paraphrase that we developed for PLANDoc, which allows for the generation of summaries that are both concise-avoiding repetition of similar information, and fluentavoiding repetition of similar phrasing

Practical issues in automatic documentation generation

PLANDoc, a system under joint development by Columbia and Bellcore, documents the activity of planning engineers as they study telephone routes. It takes as input a trace of the engineer’s interaction with a network planning tool and produces 1-2 page summary. In this paper, we describe the user needs analysis we performed and how it influenced the development of PLANDoc. In particular, we show how it pinpointed the need for a sublanguage specification, allowing us to identify input messages and to characterize the different sentence paraphrases for realizing them. We focus on the systematic use of conjunction in combination with paraphrase that we developed for PLANDoc, which allows for the generation of summaries that are both concise–avoiding repetition of similar information, and fluent– avoiding repetition of similar phrasing.