If it may have happened before, it happened, but not necessarily before

Temporal uncertainty in raw data can impede the inference of temporal and causal relationships between events and compromise the output of data-to-text NLG systems. In this paper, we introduce a framework to reason with and represent temporal uncertainty from the raw data to the generated text, in order to provide a faithful picture to the user of a particular situation. The model is grounded in experimental data from multiple languages, shedding light on the generality of the approach.peer-reviewe

SimpleNLG : a realisation engine for practical applications

This paper describes SimpleNLG, a realisation engine for English which aims to provide simple and robust interfaces to generate syntactic structures and linearise them. The library is also flexible in allowing the use of mixed (canned and noncanned) representations.peer-reviewe

The GREC main subject reference generation challenge 2009 : overview and evaluation results

The GREC-MSR Task at Generation Challenges 2009 required participating systems to select coreference chains to the main subject of short encyclopaedic texts collected from Wikipedia. Three teams submitted one system each, and we additionally created four baseline systems. Systems were tested automatically using existing intrinsic metrics. We also evaluated systems extrinsically by applying coreference resolution tools to the outputs and measuring the success of the tools. In addition, systems were tested in an intrinsic evaluation involving human judges. This report describes the GREC-MSR Task and the evaluation methods applied, gives brief descriptions of the participating systems, and presents the evaluation results.peer-reviewe

Evaluating algorithms for the generation of referring expressions using a balanced corpus

Despite being the focus of intensive research, evaluation of algorithms that generate referring expressions is still in its infancy. We describe a corpusbased evaluation methodology, applied to a number of classic algorithms in this area. The methodology focuses on balance and semantic transparency to enable comparison of human and algorithmic output. Although the Incremental Algorithm emerges as the best match, we found that its dependency on manually-set parameters makes its performance difficult to predict.peer-reviewe