In its simplest form, patent landscaping is the process of identifying all patents that are related to a particular technology or technology area. Patent landscapes are useful for a number of activities: it is important for assessing the coverage, value, or context of particular pieces of intellectual property, or for understanding the direction, speed, or concentration of innovation in a particular industry Hunt et al. [2007]. For example, companies create patent landscapes to evaluate the risks posed by competitors in a particular technology space, or to decide whether and how much to invest in pursuing particular innovations. Patent offices and economic monitoring organizations use patent landscapes to evaluate how a particular technology is affecting or might affect the economy, for example, how much economic investment is underway in a technology, how much economic value has been generated, or how many industries or companies are supported by a particular technology. Governments, in turn, can use that information to implement technology policies, for example, deciding whether to steer investment or tax incentives to companies working in particular areas (e.g., AI or green technologies). While the simplest form of patent landscaping merely identifies which patents are related to a particular area, other more sophisticated forms of patent landscaping can seek to identify how different subareas of a technology area are related, which companies or inventor groups are the most prolific, what regions are involved, or what specific types of innovations are the focus of current development.

Animacy is the characteristic of being able to independently carry out actions in a story world (e.g., movement, communication). It is a necessary property of characters in stories, and so detecting animacy is an important step in automatic story understanding. Prior approaches to animacy detection have conceived of animacy as a word- or phrase-level property, without explicitly connecting it to characters. In this work we compute the animacy of referring expressions using a statistical approach incorporating features such as word embeddings on referring expression, noun, grammatical subject and semantic roles. We then compute the animacy of coreference chains via a majority vote of the animacy of the chain's constituent referring expressions. We also reimplement prior approaches to word-level animacy to compare performance. We demonstrate these results on a small set of folktales with gold-standard annotations for coreference structure and animacy (15 Russian folktales translated into English). Folktales present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, tree that talk). We achieve an F1 measure 0.90 for the referring expression animacy model, and 0.86 for the coreference chain model. We discuss several ways in which we anticipate these results may be improved in future work.

Narratives are ubiquitous in human experience. We use them - What comprises the set of possible narrative arcs? Is there to educate, communicate, convince, explain, and entertain. How many possible story lines are there? Is As far as we know, every society in the world has narratives, there a recipe (à la Joseph Campbell or Vladimir Propp) which suggests they are rooted in our psychology and serve for generating narratives? an important cognitive function: that narratives do something - What are the appropriate representations of narrative?

On October 8-10, 2009 an interdisciplinary group met at the Wylie Center in Beverley, Massachusetts to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank.