Managing Surface Ambiguity in the Generation of Referring Expressions (Imtiaz Hussain Khan) Most algorithms for the Generation of Referring Expressions tend to generate distinguishing descriptions at the semantic level, disregarding the ways in which surface issues can affect their quality. This thesis explores the role of surface ambiguities in referring expressions and how the risk of such ambiguities should be taken into account by an algorithm that generates referring expressions. This was done by focussing on the type of surface ambiguity which arises when adjectives occur in coordinated structures (as in the old men and women). The central idea is to use statistical information about lexical co-occurrence to estimate which interpretation of a phrase is most likely for human readers, and to avoid generating phrases where misunderstandings are likely. We develop specific hypotheses, and test them by running experiments with human participants. We found that the Word Sketches are a reliable source of information to predict the likelihood of a reading. The avoidance of misunderstandings is not the only issue to be dealt with in this thesis. Since the avoidance of misunderstandings might be achieved at the cost of very lengthy (or perhaps very disfluent) expressions, it is important to select an optimal expression (i.e., the expression which is preferred by most readers) from various alternatives available. Again, we develop specific hypotheses, and recorded human preferences in a forced-choice manner. We found that participants preferred clear (i.e., not likely to be misunderstood) expressions to unclear ones, but if several of the expressions were clear then brief expressions were preferred over their longer counterparts. The results of these empirical studies motivated the design of a GRE algorithm. The implemented algorithm builds a plural distinguishing description for the intended referents (if one exists), using words; applies transformation rules to the distinguishing description to construct a set of distinguishing descriptions that are logically equivalent. Each description in the set is realised as a corresponding English noun phrase (NP) using appropriate realisation rules; the most likely reading of each NP is determined. One NP is selected for output. A further experiment verifies that the kinds of expressions produced by the algorithm are optimal for readers: they are understood accurately and quickly by readers.