ONTOGENERATION: Reusing Domain and Linguistic Ontologies for Spanish Text Generation

A significant problem facing the reuse of ontologies is to make their content more widely accessible to any potential user. Wording all the information represented in an ontology is the best way to ease the retrieval and understanding of its contents. This article proposes a general approach to reuse domain and linguistic ontologies with natural language generation technology, describing a practical system for the generation of Spanish texts in the domain of chemical substances. For this purpose the following steps have been taken: (a) an ontology in the chemicals domain developed under the METHONTOLOGY framework and the Ontology Design Environment (ODE) has been taken as knowledge source; (b) the linguistic ontology GUM (Generalized Upper Model) used in other languages has been extended and modified for Spanish; (c) a Spanish grammar has been built following the systemic-functional model by using the KPML (Komet-Penman Multilingual) environment. As result, the final system named Ontogeneration permits the user to consult and retrieve all the information of the ontology in Spanish

The VERBMOBIL domain model version 1.0

This report describes the domain model used in the German Machine Translation project VERBMOBIL. In order make the design principles underlying the modeling explicit, we begin with a brief sketch of the VERBMOBIL demonstrator architecture from the perspective of the domain model. We then present some rather general considerations on the nature of domain modeling and its relationship to semantics. We claim that the semantic information contained in the model mainly serves two tasks. For one thing, it provides the basis for a conceptual transfer from German to English; on the other hand, it provides information needed for disambiguation. We argue that these tasks pose different requirements, and that domain modeling in general is highly task-dependent. A brief overview of domain models or ontologies used in existing NLP systems confirms this position. We finally describe the different parts of the domain model, explain our design decisions, and present examples of how the information contained in the model can be actually used in the VERBMOBIL demonstrator. In doing so, we also point out the main functionality of FLEX, the Description Logic system used for the modeling

Criterios ontológicos en FunGramKB

[EN] Ontology engineering should be grounded on a protocol of well-founded guidelines concerning the structuring of the ontology as well as the elements to be included and their ontological properties. A sound methodology for ontology development involves a dramatic reduction of many common errors and inconsistencies in conceptual modelling, facilitating thus interoperatibility and knowledge sharing—particularly useful when a multipurpose resource is designed. In the natural language processing context, this paper describes the ontological commitments to which the FunGramKB Ontology is subject.[ES] Cualquier trabajo en ingeniería ontológica debe estar fundamentado en un protocolo de directrices bien definidas que no sólo organicen la estructuración de la ontología sino que además ayuden a determinar sus unidades ontológicas y propiedades. Una sólida metodología para el desarrollo de ontologías exige la eliminación de muchos de los errores e inconsistencias que se suelen cometer en el modelado ontológico, facilitando así la interoperatibilidad y el conocimiento compartido—especialmente útil cuando se diseña un recurso multipropósito. En el contexto del procesamiento del lenguaje natural, este artículo describe los compromisos ontológicos que la Ontología de FunGramKB debe cumplir.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2008-05035- C02-01/FILO. The research has been cofinanced through FEDER funds.Periñán Pascual, JC.; Arcas Túnez, F. (2010). Ontological commitments in FunGramKB. Procesamiento del Lenguaje Natural. (44):27-34. http://hdl.handle.net/10251/52170S27344

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Sources And Patterns Of Spelling Errors In Language-Learners Language : An Investigation Of Persian Learners Of English

This study investigates the sources and patterns of spelling errors of Persian English language learners. There are four major objectives. First, it attempts to determine sources of interlingual errors in the spelling of Persian English language learners. Next, it endeavors to determine sources of intralingual errors in the spelling of Persian English language learners. Then, this study will establish patterns of interlingual errors in the spelling of Persian English language learners. And finally, it will establish patterns of intralingual errors in the spelling of Persian English language learners. Forty Persian English language learners participated in this study. They have been randomly selected from the total population of 200 Persian English language learners who are studying in grade one of secondary education cycle in Daragaz, a city in Khorasan Razavi state of Iran. The data was gathered using a word dictation test. To achieve the objectives, the procedures utilized in this study for identification of spelling errors were adopted from Corder (1974)

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT