A uniform computational model for natural language parsing and generation

In the area of natural language processing in recent years, there has been a strong tendency towards reversible natural language grammars, i.e., the use of one and the same grammar for grammatical analysis (parsing) and grammatical synthesis (generation) in a natural language system. The idea of representing grammatical knowledge only once and of using it for performing both tasks seems to be quite plausible, and there are many arguments based on practical and psychological considerations for adopting such a view (in section 2.1 we discuss the most important arguments in more detail). Nevertheless, in almost all large natural language systems in which parsing and generation are considered in similar depth, different algorithms are used - even when the same grammar is used. At present, the first attempts are being made at uniform architectures which are based on the paradigm of natural language processing as deduction (they are described and discussed in section 2.3 in detail). Here, grammatical processing is performed by means of the same underlying deduction mechanism, which can be parameterized for the specific tasks at hand. Natural language processing based on a uniform deduction process has a formal elegance and results in more compact systems. There is one further advantage that is of both theoretical and practical relevance: a uniform architecture offers the possibility of viewing parsing and generation as strongly interleaved tasks. Interleaving parsing and generation is important if we assume that natural language understanding and production are not performed in an isolated way but rather can work together to obtain a flexible use of language. In particular this means a.) the use of one mode of operation for monitoring the other and b.) the use of structures resulting from one direction directly in the other. For example, during generation integrated parsing can be used to monitor the generation process and to cause some kind of revision, e.g., to reduce the risk of misunderstandings. Research on monitoring and revision strategies is a very active area in cognitive science; however, currently there exists no algorithmic model of such a behaviour. A uniform architecture can be an important step in that direction. Unfortunately, the currently proposed uniform architectures are very inefficient and it is yet unclear how an efficiency-oriented uniform model could be achieved. An obvious problem is that in each direction different input structures are involved - a string for parsing and a semantic expression for generation - which causes a different traversal of the search space defined by the grammar. Even if this problem were solved, it is not that obvious how a uniform model could re-use partial results computed in one direction efficiently in the other direction for obtaining a practical interleaved approach to parsing and generation.Liegt nicht vor

A uniform computational model for natural language parsing and generation

In the area of natural language processing in recent years, there has been a strong tendency towards reversible natural language grammars, i.e., the use of one and the same grammar for grammatical analysis (parsing) and grammatical synthesis (generation) in a natural language system. The idea of representing grammatical knowledge only once and of using it for performing both tasks seems to be quite plausible, and there are many arguments based on practical and psychological considerations for adopting such a view (in section 2.1 we discuss the most important arguments in more detail). Nevertheless, in almost all large natural language systems in which parsing and generation are considered in similar depth, different algorithms are used - even when the same grammar is used. At present, the first attempts are being made at uniform architectures which are based on the paradigm of natural language processing as deduction (they are described and discussed in section 2.3 in detail). Here, grammatical processing is performed by means of the same underlying deduction mechanism, which can be parameterized for the specific tasks at hand. Natural language processing based on a uniform deduction process has a formal elegance and results in more compact systems. There is one further advantage that is of both theoretical and practical relevance: a uniform architecture offers the possibility of viewing parsing and generation as strongly interleaved tasks. Interleaving parsing and generation is important if we assume that natural language understanding and production are not performed in an isolated way but rather can work together to obtain a flexible use of language. In particular this means a.) the use of one mode of operation for monitoring the other and b.) the use of structures resulting from one direction directly in the other. For example, during generation integrated parsing can be used to monitor the generation process and to cause some kind of revision, e.g., to reduce the risk of misunderstandings. Research on monitoring and revision strategies is a very active area in cognitive science; however, currently there exists no algorithmic model of such a behaviour. A uniform architecture can be an important step in that direction. Unfortunately, the currently proposed uniform architectures are very inefficient and it is yet unclear how an efficiency-oriented uniform model could be achieved. An obvious problem is that in each direction different input structures are involved - a string for parsing and a semantic expression for generation - which causes a different traversal of the search space defined by the grammar. Even if this problem were solved, it is not that obvious how a uniform model could re-use partial results computed in one direction efficiently in the other direction for obtaining a practical interleaved approach to parsing and generation.Liegt nicht vor

Introduction to the Special Issue on Incremental Processing in Dialogue

  A brief introduction to the topics discussed in the special issue, and to the individual papers

Introduction to the Special Issue

A brief introduction to the topics discussed in the special issue, and to the individual papers

EL GESE: UN GENERADOR DE ENUNCIADOS SIMPLES EN ESPAÑOL BASADO EN EL MODELO SEMÁNTICO PRAGMÁTICO DE LA LINGÜÍSTICA APLICADA

La investigación que aquí se presenta tiene como resultado un dispositivoinformático: el GESE Generador de Enunciados Simples en Español. Dentro delámbito de la Lingüística Computacional, se denomina "generador" a un programa decomputador capaz de producir enunciados en "lenguaje natural” a partir deinferencias. Para lograr tal fin, el generador desarrollado tiene como sustentolingüístico el llamado Modelo Semántico - Pragmático, perteneciente a la lingüísticaaplicada, que as u vez se fundamenta en la Semántica Generativa. El fuerte de lainvestigación radica en el desarrollo tecnológico mismo, así como en la validación delmodelo Semántico Pragmático en un nuevo campo del saber: la LingüísticaComputacional. La investigación demuestra que dicho modelo es adecuado ypotente como soporte para desarrollar programas orientados al procesamientocomputacional de lenguaje natural, más específicamente en el área de estudiollamada "generación de lenguaje" (language generation). Por otra parte, la aplicacióntecnológica presentada (GESE) permite verificar que PROLOG es una excelenteherramienta para el desarrollo de este tipo de programas

Interleaving natural language parsing and generation through uniform processing

We present a new model of natural language processing in which natural language parsing and generation are strongly interleaved tasks. Interleaving of parsing and generation is important if we assume that natural language understanding and production are not only performed in isolation but also can work together to obtain subsentential interactions in text revision or dialog systems. The core of the model is a new uniform agenda-driven tabular algorithm, called UTA. Although uniformly defined, UTA is able to configure itself dynamically for either parsing or generation, because it is fully driven by the structure of the actual input - a string for parsing and a semantic expression for generation. Efficient interleaving of parsing and generation is obtained through item sharing between parsing and generation. This novel processing strategy facilitates exchanging items (i.e., partial results) computed in one direction automatically to the other direction as well. The advantage of UTA in combination with the item sharing method is that we are able to extend the use of memorization techniques even to the case of an interleaved approach. In order to demonstrate UTA\u27s utility for developing high-level performance methods, we present a new algorithm for incremental self-monitoring during natural language production

Rollenübernahme als Benutzermodellierungsmethode : globale Antizipation in einem transmutierbaren Dialogsystem

In dieser Arbeit werden zwei Aspekte pragmatisch ausgerichteten Dialogverhaltens untersucht: Eine flexible Interaktion zwischen den Systemkomponenten und die Fähigkeit eines Dialogsystems, innerhalb einer gegebenen Dialogsituation mehr als eine der Dialogrollen zu übernehmen. Während das erste Ziel durch eine Multi-Agenten-Architektur unterstützt wird, bezieht sich das zweite Ziel auf die Fähigkeit, sich gedanklich in die Lage der am Dialog beteiligten Partner zu versetzen, um deren Reaktionen bzw. Intentionen zu antizipieren. Diese Strategie der globalen Antizipationsrückkopplung wurde in dem PRACMA- System realisiert, das in einem Verkaufsgespräch sowohl die Rolle des Verkäufers als auch die des Käufers übernehmen kann. Es wird gezeigt, wie die globale Antizipation zur Vorhersage des Käufers- bzw. Verkäufersverhaltens in PRACMA implementiert wurde. Es wird auch ein Ansatz präsentiert, der der Unsicherheit über das Dialogverhalten und die Präferenzen des Dialogpartners Rechnung trägt. Schließlich werden einige Überlegungen zur Effizienz bei der Verwendung der globalen Antizipationsrückkopplung in Abhängigkeit von den verfügbaren Systemressourcen erörtert. Schlüsselwörter: Antizipationsrückkopplung, Rollenübernahme, Multi-Agenten-Architektur, Benutzermodellierung, natürlichsprachliche Verarbeitung, Transmutabilität.Two characteristics of pragmatically oriented dialog processing are investigated in this thesis: Flexible cooperation among the system\u27s modules, which maximizes the system\u27s exploitation of its knowledge and of its reasoning capabilities; and the ability of a system to take either (or any) of the dialog roles in its domain. While attainment of the first goal is supported by a multi-agent architecture, the second goal focuses on the ability of dialog participants to predict the responses of their dialog partners by hypothetically assuming the partner\u27s role. This strategy, called global anticipation feedback, is investigated within PRACMA, a dialog system that is capable of taking either role (buyer or seller) in a sales talk. It is shown how global anticipation feedback can be used to anticipate either the seller\u27s or the buyer\u27s behavior. An extension of these techniques is discussed that addresses the limited predictability of a dialog partner\u27s responses. Finally, several approaches to minimizing the computational cost of using global anticipation feedback according to the available system resources are addressed

A uniform computational model for natural language parsing and generation

SIGLEAvailable from TIB Hannover: RS 1516(1) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Integrating deep and shallow natural language processing components : representations and hybrid architectures

We describe basic concepts and software architectures for the integration of shallow and deep (linguistics-based, semantics-oriented) natural language processing (NLP) components. The main goal of this novel, hybrid integration paradigm is improving robustness of deep processing. After an introduction to constraint-based natural language parsing, we give an overview of typical shallow processing tasks. We introduce XML standoff markup as an additional abstraction layer that eases integration of NLP components, and propose the use of XSLT as a standardized and efficient transformation language for online NLP integration. In the main part of the thesis, we describe our contributions to three hybrid architecture frameworks that make use of these fundamentals. SProUT is a shallow system that uses elements of deep constraint-based processing, namely type hierarchy and typed feature structures. WHITEBOARD is the first hybrid architecture to integrate not only part-of-speech tagging, but also named entity recognition and topological parsing, with deep parsing. Finally, we present Heart of Gold, a middleware architecture that generalizes WHITEBOARD into various dimensions such as configurability, multilinguality and flexible processing strategies. We describe various applications that have been implemented using the hybrid frameworks such as structured named entity recognition, information extraction, creative document authoring support, deep question analysis, as well as evaluations. In WHITEBOARD, e.g., it could be shown that shallow pre-processing increases both coverage and efficiency of deep parsing by a factor of more than two. Heart of Gold not only forms the basis for applications that utilize semanticsoriented natural language analysis, but also constitutes a complex research instrument for experimenting with novel processing strategies combining deep and shallow methods, and eases replication and comparability of results.Diese Arbeit beschreibt Grundlagen und Software-Architekturen für die Integration von flachen mit tiefen (linguistikbasierten und semantikorientierten) Verarbeitungskomponenten für natürliche Sprache. Das Hauptziel dieses neuartigen, hybriden Integrationparadigmas ist die Verbesserung der Robustheit der tiefen Verarbeitung. Nach einer Einführung in constraintbasierte Analyse natürlicher Sprache geben wir einen Überblick über typische Aufgaben flacher Sprachverarbeitungskomponenten. Wir führen XML Standoff-Markup als zusätzliche Abstraktionsebene ein, mit deren Hilfe sich Sprachverarbeitungskomponenten einfacher integrieren lassen. Ferner schlagen wir XSLT als standardisierte und effiziente Transformationssprache für die Online-Integration vor. Im Hauptteil der Arbeit stellen wir unsere Beiträge zu drei hybriden Architekturen vor, welche auf den beschriebenen Grundlagen aufbauen. SProUT ist ein flaches System, das Elemente tiefer Verarbeitung wie Typhierarchie und getypte Merkmalsstrukturen nutzt. WHITEBOARD ist das erste System, welches nicht nur Part-of-speech-Tagging, sondern auch Eigennamenerkennung und flaches topologisches Parsing mit tiefer Verarbeitung kombiniert. Schließlich wird Heart of Gold vorgestellt, eine Middleware-Architektur, welche WHITEBOARD hinsichtlich verschiedener Dimensionen wie Konfigurierbarkeit, Mehrsprachigkeit und Unterstützung flexibler Verarbeitungsstrategien generalisiert. Wir beschreiben verschiedene, mit Hilfe der hybriden Architekturen implementierte Anwendungen wie strukturierte Eigennamenerkennung, Informationsextraktion, Kreativitätsunterstützung bei der Dokumenterstellung, tiefe Frageanalyse, sowie Evaluationen. So konnte z.B. in WHITEBOARD gezeigt werden, dass durch flache Vorverarbeitung sowohl Abdeckung als auch Effizienz des tiefen Parsers mehr als verdoppelt werden. Heart of Gold bildet nicht nur Grundlage für semantikorientierte Sprachanwendungen, sondern stellt auch eine wissenschaftliche Experimentierplattform für weitere, neuartige Kombinationsstrategien dar, welche zudem die Replizierbarkeit und Vergleichbarkeit von Ergebnissen erleichtert