Leo Wanner

A development Environment for an MTT-Based Sentence Generator

With the rising standard of the state of the art in text generation and the increase of the number of practical generation applications, it becomes more and more important to provide means for the maintenance of the generator, i.e. its extension, modification, and monitoring by grammarians who are not familiar with its internals. However, only a few sentence and text generators developed to date actually provide these means. One of these generators is KPML (Bateman, 1997). KPML comes with a Development Environment and there is no doubt about the contribution of this environment to the popularity of the systemic approach in generation.

MARQUIS: GENERATION OF USER-TAILORED MULTILINGUAL AIR QUALITY BULLETINS

Air pollution has a major influence on health. It is thus not surprising that air quality (AQ) increasingly becomes a central issue in the environmental information policy worldwide. The most common way to deliver AQ information is in terms of graphics, tables, pictograms, or color scales that display either the concentrations of the pollutant substances or the corresponding AQ indices. However, all of these presentation modi lack the explanatory dimension; nor can they be easily tailored to the needs of the individual users. MARQUIS is an AQ information generation service that produces user-tailored multilingual bulletins on the major measured and forecasted air pollution substances and their relevance to human health in five European regions. It incorporates modules for the assessment of pollutant time series episodes with respect to their relevance to a given addressee, for planning of the discourse structure of the bulletins and the selection of the adequate presentation mode, and for generation proper. The positive evaluation of the bulletins produced by MARQUIS by users shows that the use of automatic text generation techniques in such a complex and sensitive application is feasible.

Towards automatic fine-grained semantic classification of verb-noun collocations

Plain lists of collocations as provided to date by most approaches to automatic acquisition of collocations from corpora are useful as a resource for dictionary construction. However, their use is rather limited in the case of NLP-applications such as Text Generation, Machine Translation and Text Summarization if not enriched by information on the grammatical function of the collocation elements and by information on the semantics of the collocations as multiword units. In this article, we describe an approach to a fine-grained classification of verb-noun bigrams according to a semantically motivated typology of collocations and illustrate this with Spanish material. The typology of collocations that underlies our classification is based on verb-noun Lexical Functions (LFs) from the Explanatory Combinatorial Lexicology. In the first stage of the approach, the program learns the semantic features of each LF from training data. In the second stage, it examines the semantic features of verb-noun candidate bigrams and compares them with the features of all the LFs taken into account. A candidate whose features are sufficiently similar to those of a specific LF is considered to be an instance of this LF. The semantic features of both the training material and the candidate bigrams are derived from the hyperonymy hierarchies provided by the EuroWordNet. In the experiments carried out to validate the approach, we achieved an average

From ontology to NL: generation of multilingual user-oriented environmental reports

f

Syntactic mismatches in machine translation

-score of about 70%.

Building an Environmental Information System for Personalized Content Delivery

Natural Language Generation (NLG) from knowledge bases (KBs) has repeatedly been subject of research. However, most proposals tend to have in common that they start from KBs of limited size that either already contain linguistically-oriented knowledge structures or to whose structures different ways of realization are explicitly assigned. To avoid these limitations, we propose a three layer OWL-based ontology framework in which domain, domain communication and linguistic knowledge structures are clearly separated and show how a large scale instantiation of this framework in the environmental domain serves multilingual NLG.

Making sense of collocations

This paper addresses one of the central problems arising at the transfer stage in machine translation: syntactic mismatches, that is, mismatches between a source-language sentence structure and its equivalent target-language sentence structure. The level at which we assume the transfer to be carried out is the Deep-Syntactic Structure (DSyntS) as proposed in the Meaning-Text Theory (MTT). DSyntS is abstract enough to avoid all types of divergences that result either from restricted lexical co-occurrence or from surface-syntactic discrepancies between languages. As for the remaining types of syntactic divergences, all of them occur not only interlinguistically, but also intralinguistically; this means that establishing correspondences between semantically equivalent expressions of the source and target languages that diverge with respect to their syntactic structure is nothing else than paraphrasing. This allows us to adapt the powerful intralinguistic paraphrasing mechanism developed in MTT for purposes of interlinguistic transfer.

On using a parallel graph rewriting formalism in generation

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

Perspective-oriented generation of football match summaries: Old tasks, new challenges

Lexico-semantic collocations (LSCs) are a prominent type of multiword expressions. Over the last decade, the automatic compilation of LSCs from text corpora has been addressed in a significant number of works. However, very often, the output of an LSC-extraction program is a plain list of LSCs. Being useful as raw material for dictionary construction, plain lists of LSCs are of a rather limited use in NLP-applications. For NLP, LSCs must be assigned syntactic and, especially, semantic information. Our goal is to develop an ‘‘off-the-shelf’’ LSC-acquisition program that annotates each LSC identified in the corpus with its syntax and semantics. In this article, we address the annotation task as a classification task,viewing it as a machine learning problem. The LSC-typology we use are the lexical functions from the Explanatory Combinatorial Lexicology; as lexico-semantic resource, EuroWordnet has been used. The applied machine learning technique is a variant of the nearest neighbor-family, which is defined over lexico-semantic features of the elements of LSCs. The technique has been tested on Spanish verb–noun bigrams. � 2005 Elsevier Ltd. All rights reserved.

FootbOWL: using a generic ontology of football competition for planning match summaries

In this paper, we present a parallel context sensitive graph rewriting formalism for a dependency-oriented generation grammar. The parallel processing of the input structure makes an explicit presentation of all alternative options for its mapping onto the output structure possible. This allows for the selection of the linguistic realization that suits best the communicative and contextual criteria available.