The definitions of the basic concepts, rules, and constraints of centering theory involve underspecified notions such as 'previous utterance', 'realization', and 'ranking'. We attempted to find the best way of defining each such notion among those that can be annotated reliably, and using a corpus of texts in two domains of practical interest. Our main result is that trying to reduce the number of utterances without a backward-looking center (CB) results in an increased number of cases in which some discourse entity, but not the CB, gets pronominalized, and viceversa.

It is not a rare phenomenon for human written text to use non-restrictive NP modifiers to express essential pieces of information or support the situation presented in the main proposition containing the NP, for example, "Private Eye, which couldn't afford the libel payment, had been threatened with closure."(from Wall Street Journal) Yet no previous research in NLG investigates this in detail.This paper describes corpus analysis and a psycholinguistic experiment regarding the acceptability of using non-restrictive NP modifiers to express semantic relations that might normally be signalled by 'because' and 'then'.The experiment tests several relevant factors and enables us to accept or reject a number of hypotheses.The results are incorporated into an NLG system based on a Genetic Algorithm.

In natural language generation, different generation tasks often interact with each other in a complex way. We think that how to resolve the complex interactions inside and between tasks is more important to the generation of a coherent text than how to model each individual factor. This paper focuses on the interaction between aggregation and text planning, and tries to explore what preferences exist among the features considered by the two tasks. The preferences are implemented in two generation systems, namely ILEX-TS and a text planner using a Genetic Algorithm. The evaluation emphasises the second implementation and shows that capturing these preferences properly can lead to coherent text.