Context is used in many NLP systems as an indicator of a term's syntactic and semantic function. The accuracy of the system is dependent on the quality and quantity of contextual information available to describe each term. However, the quantity variable is no longer fixed by limited corpus resources. Given fixed training time and computational resources, it makes sense for systems to invest time in extracting high quality contextual information from a fixed corpus. However, with an effectively limitless quantity of text available, extraction rate and representation size need to be considered. We use thesaurus extraction with a range of context extracting tools to demonstrate the interaction between context quantity, time and size on a corpus of 300 million words.

In this article we propose a strategy for the summarization of scientific articles that concentrates on the rhetorical status of statements in an article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source article and situate it with respect to earlier work. We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics annotated with human judgments of the rhetorical status and relevance of each sentence in the articles. We present several experiments measuring our judges' agreement on these annotations. We also present an algorithm that, on the basis of the annotated training material, selects content from unseen articles and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it provides starting material for the generation of task-oriented and user-tailored summaries designed to give users an overview of a scientific field.

We believe that identifying the structure of scientific argumentation in articles can help in tasks such as automatic summarization or the automated construction of citation indexes. One particularly important aspect of this structure is the question of who a given scientific statement is attributed to: other researchers, the field in general, or the authors themselves.We present the algorithm and a systematic evaluation of a system which can recognize the most salient textual properties that contribute to the global argumentative structure of a text. In this paper we concentrate on two particular features, namely the occurrences of prototypical agents and their actions in scientific text.

We describe an implementation in Carpenter's typed feature formalism, ALE, of a discourse grammar of the kind proposed by Scha, Polanyi, et al.We examine their method for resolving

A semantics of linguistic categories like tense, aspect, and certain temporal adverbials, and a theory of their use in defining the temporal relations of events, both require a more complex structure on the domain underlying the meaning representations than is commonly assumed. The paper proposes an ontology based on such notions as causation and consequence, rather than on purely temporal primitives. We claim that any manageable logic or other formal system for natural language temporal descriptions will have to embody such an ontology, as will any usable temporal database for knowledge about events which is to be interrogated using natural language.