Understanding Implicit Entities and Events with Getaruns

Semantic processing represents the new challenge for all applications that require text understanding, as for instance Q/A. In this paper we will highlight the need to couple statistical approaches with deep linguistic processing and will focus on “implicit” or lexically unexpressed linguistic elements that are nonetheless necessary for a complete semantic interpretation of a text. We will address the following types of “implicit” entities and events: - grammatical ones, as suggested by a linguistic theories like LFG or similar generative theories; - semantic ones suggested in the FrameNet project, i.e. CNI, DNI, INI; - pragmatic ones: here we will present a theory and an implementation for the recovery of implicit entities and events of (non-) standard implicatures. In particular we will show how the use of commonsense knowledge may fruitfully contribute in finding relevant implied meanings. We will also briefly explore the Subject of Point of View which is computed by Semantic Informational Structure and contributes the intended entity from whose point of view is expressed a given subjective statement. We also present an evaluation based on section 24 of Penn Treebank as encoded by LFG people in the PARC-700 treebank where lexically unexpressed are adequately classified and diversified

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

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