Much of the data found on the world wide web is in numeric, tabular, or other nontextual format (e.g., weather forecast tables, stock market charts, live sensor feeds), and thus inaccessible to non-experts or laypersons. However, most conventional search engines and natural language processing tools (e.g., summarisers) can only handle textual input. As a result, data in non-textual form remains largely inaccessible. Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input, and holds promise for rendering non-linguistic data widely accessible. Several successful generation systems have been produced in the past twenty years. They mostly rely on human-crafted rules or expert-driven grammars, implement a pipeline architecture, and usually operate in a single domain. In this thesis, we present several novel statistical models that take as input a set of database records and generate a description of them in natural language text. Our unique idea is to combine the processes of structuring a document (document planning), deciding what to say (content selection) and choosing the specific words and syntactic constructs specifying how to say it (lexicalisation and surface realisation), in a uniform joint manner. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). This joint representation allows individual processes (i.e., document planning, content selection, and surface realisation) to communicate and influence each other naturally. We recast generation as the task of finding the best derivation tree for a set of input database records and our grammar, and describe several algorithms for decoding in this framework that allows to intersect the grammar with additional information capturing fluency and syntactic well-formedness constraints. We implement our generators using the hypergraph framework. Contrary to traditional systems, we learn all the necessary document, structural and linguistic knowledge from unannotated data. Additionally, we explore a discriminative reranking approach on the hypergraph representation of our model, by including more refined content selection features. Central to our approach is the idea of porting our models to various domains; we experimented on four widely different domains, namely sportscasting, weather forecast generation, booking flights, and troubleshooting guides. The performance of our systems is competitive and often superior compared to state-of-the-art systems that use domain specific constraints, explicit feature engineering or labelled data.

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Many natural language processing applications, such as information extraction, question answering, topic detection and tracking, would benefit significantly from the ability to accurately position reported events in time, either relatively with respect to other events or absolutely with respect to calendrical time. However, relatively little work has been done to date on the automatic extraction of temporal information from text. Before we can progress to automatically position reported events in time, we must gain an understanding of the mechanisms used to do this in language. This understanding can be promoted through the development of all annotation scheme, which allows us to identify the textual expressions conveying events, times and temporal relations in a corpus of 'real' text. This thesis describes a fine-grained annotation scheme with which we can capture all events, times and temporal relations reported ill a text. To aid the application of the scheme to text, a graphical annotation tool has been developed. This tool not only allows easy markup of sophisticated temporal annotations, it also contains an interactive, inference-based component supporting the gathering of temporal relations. The annotation scheme and the tool have been evaluated through the construction of a trial corpus during a pilot study. In this study, a group of annotators was supplied with a description of the annotation scheme and asked to apply it to a trial corpus. The pilot study showed that the annotation scheme was difficult to apply, but is feasible with improvements to the definition of the annotation scheme and the tool. Analysis of the resulting trial corpus also provides preliminary results on the relative extent to which different linguistic mechanisms, explicit and implicit, are used to convey temporal relational information in text.

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