Introduction

Interdisciplinary investigations marry the methods and concerns of different fields. Computer science is the study of precise descriptions of finite processes; semantics is the study of meaning in language. Thus, computational semantics embraces any project that approaches the phenomenon of meaning by way of tasks that can be performed by following definite sets of mechanical instructions. So understood, computational semantics revels in applying semantics, by creating intelligent devices whose broader behavior fits the meanings of utterances, and not just their form. IBM's Watson (Ferrucci et al., 2010) is a harbinger of the excitement and potential of this technology.

In applications, the key questions of meaning are the questions engineers must answer to make things work. How can we build a system that copes robustly with the richness and variety of meaning in language, and with its ambiguities and underspecification? The focus is on methods that give us clear problems we can state and solve.

Language is large, and it is often a messy but routine business to create program constructs that account for linguistic data as well as possible given the background constraints otherwise imposed on system design. So the bread and butter of computational semantics is the development of machine-learning methods to induce and disambiguate symbolic structures that capture aspects of meaning in language. Linguistic issues are often secondary. I will not attempt a survey of machine learning in computational linguistics, or even computational semantics, here. Useful starting points into the literature are Manning and Schütze (1999), Jurafsky and Martin (2008), Marquez et al. (2008), Agirre et al. (2009).

Instead, in keepingwith the emphasis of this volume, I will explore the scientific overlap of computer science and semantics. My experience has always confirmed a deep affinity between the perspectives of the two fields. Recent developments in computational semantics bring an exciting new suite of tools, resources, and methods to the scene. These results promise not only to enliven the capabilities of the robots it seems we must inevitably talk to but also to profoundly enrich our understanding of meaning in language as a unique bridge between the physical, psychological, and social worlds.