Chapter 7 turns our attention to basic word order in language and natural order of thought. In his seminal work, Greenberg (1963) observed that a vast majority of the world’s languages have one of the SO word orders as their basic word order. It is interesting to note that the distribution is heavily biased even among the three SO orders, with SOV being the most frequent, which indicates that SOV has some special status among the six possible word orders in some sense. Why should this be the case? To address this question, Goldin-Meadow et al. (2008) showed short animations depicting transitive events (e.g., a girl twisting a knob) to speakers of four languages (Chinese, English, Spanish [all SVO], and Turkish [SOV]). The participants were then asked to describe the depicted events by using only their hands, i.e., with gestures. The speakers of all four languages dominantly used the agent–patient–action order in their gestures, regardless of the basic word order of their languages. Goldin-Meadow et al. (2008: 9167) took these results to suggest that the agent–patient–action order reflects the natural sequencing of an event representation and that developing languages use it as the default pattern, thus displaying an SOV word order.

This chapter reviews the research conducted on the representation of events, from theperspectives ofnatural language processing, artificial intelligence (AI), and linguistics. AI approaches to modeling change have traditionally focused on situations and state descriptions. Linguistic approaches start with the description of the propositional content of sentences (or natural language expressions generally). As a result, the focus in the two fields has been on different problems. I argue that these approaches have common elements that can be drawn on to view event semantics from a unifying perspective, where we can distinguish between the surface events denoted by verbal predicates and what I refer to as the latent event structure of a sentence. By clearly distinguishing between surface and latent event structures of sentences and texts, we move closer to a general computational theory of event structure, one permitting a common vocabularyfor events and the relations between them, while enabling reasoning at multiple levels of interpretation.

In the past we experimented with variations of an approach we call semantic storytelling, in which we use multiple text analytics components including named entity recognition and event detection. This chapter summarizes some of our previous work with an emphasis on the detection of movement action events, and describes the long-term semantic storytelling vision as well as the setup and approach of our future work towards a robust technical solution, which is primarily driven by three industry use cases. Ultimately, we plan to contribute an implemented approach for semantic storytelling that makes use of various analytics services and that can be deployed in a flexible way in various industrial production environments.