Grammaticalized temporal categories, language specificity, and macroplanning in expository texts

Identifying Information Structure in Expository Texts

Identification of ‘information structure’ (organization of information within an utterance) is crucial for processing contextually-appropriate linguistic forms in many NLP systems. This paper presents a theory and a procedure to identify information structure in expository texts in English, and also an evaluation method that is used to validate the results of the identification process for real...

Temporal expression normalisation in natural language texts

Automatic annotation of temporal expressions is a research challenge of great interest in the field of information extraction. In this report, I describe a novel rule-based architecture, built on top of a preexisting system, which is able to normalise temporal expressions detected in English texts. Gold standard temporally-annotated resources are limited in size and this makes research difficul...

Coherence structure and lexical cohesion in expository and persuasive texts

Texplore - Exploring Expository Texts Via Hierarchical Representation

Exploring expository texts presents an interesting and important challenge. They are read routinely and extensively in the form of online newspapers, web-based articles, reports, technical and academic papers. We present a system, called Texplore, which assists readers in exploring the content of expository texts. The system provides two mechanisms for text exploration, an expandable outline th...

CATENA: CAusal and TEmporal relation extraction from NAtural language texts

We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machinelearned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and Ti...