GenLeNa: Sistema para la construcción de Aplicaciones de Generación de Lenguaje Natural

In this article the proposal is made for the division of the process of construction of natural language generation (NLG) systems into two stages: content planning (CP), which is dependent on the mastery of the application to be developed, and document structuring (DS). This division allows people who are not expert in NLG to develop natural language generation systems, concentrating on building abstract representations of the information to be communicated (called messages). Specific architecture for the DS stage is also presented. This enables NLG researchers to work ortogonally on specific techniques and methodologies for the conversion of messages into text which is grammatically and syntactically correct.En este artículo se propone la división del proceso de construcción de sistemas de Generación de Lenguajes Natural (GLN) en dos etapas: planificación del contenido (EPC), que es dependiente del dominio de la aplicación a desarrollar, y estructuración del documento (EED). Esta división permite que personas no expertas en GLN puedan desarrollar sistemas de generación de lenguajes natural enfocándose en construir representaciones abstractas de la información que se desea comunicar (denominadas mensajes). Adicionalmente se presenta una arquitectura específica para la etapa EED que permite a investigadores en GLN trabajar ortogonalmente en técnicas y metodologías específicas para la transformación de los mensajes en texto gramatical y sintácticamente correcto

Generierung natürlicher Sprache

Dieser Aufsatz beschreibt das interdisziplinäre Forschungsgebiet Generierung natürlicher Sprache und gibt einen Überblick über den gegenwärtigen Stand der Kunst. Behandelt werden Ansätze aus der Psycholinguistik, Planungs- und Entscheidungsverfahren aus der sprachverarbeitenden KI und Verfahren auf der Grundlage moderner Grammatikformalismen. Die jeweiligen Forschungsziele und -methoden werden dargestellt.This report describes the interdisciplinary research field of natural language generation and gives an overview of the current state of the art. The paper presents psycholinguistic approaches, AI planning and decision-making processes, and generators based on modern grammar formalisms. For each case, the research goals and methods are described

Natural language generation in the LOLITA system an engineering approach

Natural Language Generation (NLG) is the automatic generation of Natural Language (NL) by computer in order to meet communicative goals. One aim of NL processing (NLP) is to allow more natural communication with a computer and, since communication is a two-way process, a NL system should be able to produce as well as interpret NL text. This research concerns the design and implementation of a NLG module for the LOLITA system. LOLITA (Large scale, Object-based, Linguistic Interactor, Translator and Analyser) is a general purpose base NLP system which performs core NLP tasks and upon which prototype NL applications have been built. As part of this encompassing project, this research shares some of its properties and methodological assumptions: the LOLITA generator has been built following Natural Language Engineering principles uses LOLITA's SemNet representation as input and is implemented in the functional programming language Haskell. As in other generation systems the adopted solution utilises a two component architecture. However, in order to avoid problems which occur at the interface between traditional planning and realisation modules (known as the generation gap) the distribution of tasks between the planner and plan-realiser is different: the plan-realiser, in the absence of detailed planning instructions, must perform some tasks (such as the selection and ordering of content) which are more traditionally performed by a planner. This work largely concerns the development of the plan- realiser and its interface with the planner. Another aspect of the solution is the use of Abstract Transformations which act on the SemNet input before realisation leading to an increased ability for creating paraphrases. The research has lead to a practical working solution which has greatly increased the power of the LOLITA system. The research also investigates how NLG systems can be evaluated and the advantages and disadvantages of using a functional language for the generation task