This book documents the first serious attempt to construct automatically and use
nonsemantic computational structures for text summarization. Until now, most discourse …

We derive the rhetorical structures of texts by means of two new, surface-form-based
algorithms: one that identifies discourse usages of cue phrases and breaks sentences into …

This paper investigates a machine learning approach for temporally ordering and anchoring
events in natural language texts. To address data sparseness, we used temporal reasoning …

Considering empirical evidence from a free-word-order language (German) we propose a
revision of the principles guiding the ordering of discourse entities in the forward-looking …

Temporal common sense has applications in AI tasks such as QA, multi-document
summarization, and human-AI communication. We propose the task of sequencing--given a …

Interpreting Motion presents an integrated perspective on how language structures constrain
concepts of motion and how the world shapes the way motion is linguistically expressed …

The growing interest in practical NLP applications such as text summarization and question-
answering places increasing demands on the processing of temporal information in natural …

In this paper we consider the crucial problem of providing 'coherence relation'analyses for
natural texts. Among the several accounts currently being pursued, claims of broad …

Events formulate the world of the human being and could be regarded as the semantic units
in different granularities for information organization. Extracting events and temporal …

Aspectual classification maps verbs to a small set of primitive categories in order to reason
about time. This classification is necessary for interpreting temporal modifiers and assessing …