~i%xt understanding and high quality machine translation often necessitate the disambiguation of ambigous structures or lexical elements. Drawing inferences from the context can be a means for resolving semantic ambiguities. However, often, this is an ex-. pensive strategy that, in addition, not always comes up with a clear preference for one of the alternatives.In this paper, we argue that in a number of cases deep semantic analyses can be avoided by taking into account the constraints that the alternative readings impose onto the information structure. To this end, we present a study of the arnbigous German adverb erst and point out the particular circumstances under which the given information structure disambiguates the adverb without further semantic analysis.
A proposal to deal with French tenses in the framework of Discourse Representation Theory is presented, as it has been implemented for a fragment at the IMS. It is based on the theory of tenses of H. Kamp and Ch. Rohrer. Instead of using operators to express the meaning of the tenses the Reichenbachian point of view is adopted and refined such that the impact of the tenses with respect to the meaning of the text is understood as contribution to the integration of the events of a sentence in the event structure of the preceeding text. Thereby a system of relevant times provided by the preceeding text and by the temporal adverbials of the sentence being processed is used. This system consists of one or more reference times and ~emporal perspective ~imes, the speech time and the location time. The special interest of our proposal is to establish a plausible choice of "anchors" for the new event out of the system of relevant times and to update this system of temporal coordinates correctly. The problem of choice is largely neglected in the literature. In opposition to the approach of Kamp and Rohrer the exact meaning of the tenses is fixed by the resolution component and not in the process of syntactic analysis.
Lexical and morphological ambiguities present a serious challenge in rule-based machine translation (RBMT). This chapter describes an approach to resolve morphologically ambiguous verb forms if a rule-based decision is not possible due to parsing or tagging errors. The rule-based core system has a set of rules to decide, based on context information, which verb form should be generated in the target language. However, if the parse tree is not correct, part of the context information might be missing and the rules cannot make a safe decision. In this case, we use a classifier to assign a verb form. We tested the classifier on a set of four texts, increasing the correct verb forms in the translation from 78.68
Linguistic resources available in the public domain, such as lemmatisers, part-ofspeech taggers and parsers can be used for the development of MT systems: as separate processing modules or as anno
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