A Flexible Shallow Approach to Text Generation

In order to support the efficient development of NL generation systems, two orthogonal methods are currently pursued with emphasis: (1) reusable, general, and linguistically motivated surface realization components, and (2) simple, task-oriented template-based techniques. In this paper we argue that, from an application-oriented perspective, the benefits of both are still limited. In order to improve this situation, we suggest and evaluate shallow generation methods associated with increased flexibility. We advise a close connection between domain-motivated and linguistic ontologies that supports the quick adaptation to new tasks and domains, rather than the reuse of general resources. Our method is especially designed for generating reports with limited linguistic variations.Comment: LaTeX, 10 page

A Best-First Search Algorithm for Generating Referring Expressions

Existing algorithms for generating referential descriptions to sets of objects have serious deficits: while incremental approaches may produce ambiguous and redundant expressions, exhaustive searches are computationally expensive. Mediating between these extreme control regimes, we propose a best-first searching algorithm for uniquely identifying sets of objects. We incorporate linguistically motivated preferences and several techniques to cut down the search space. Preliminary results show the effectiveness of the new algorithm

An Algorithm For Generating Referential Descriptions With Flexible Interfaces

Most algorithms dedicated to the generation of referential descriptions widely suffer from a fundamental problem: they make too strong assumptions about adjacent processing components, resulting in a limited coordination with their perceptive and linguistics data, that is, the provider for object descriptors and the lexical expression by which the chosen descriptors is ultimately realized. Motivated by this deficit, we present a new algorithm that (1) allows for a widely unconstrained, incremental, and goal-driven selection of descriptors, (2) integrates linguistic constraints to ensure the expressibility of the chosen descriptors, and (3) provides means to control the appearance of the created referring expression. Hence, the main achievement of our approach lies in providing a core algorithm that makes few assumptions about other processing components and improves the flow of control between modules