NLP

The Digital Democracy platform obtains data about the legislative committee hearings: the video archives, the information about the state legislature and so on. Figure 1 shows the design of the DD system. The main source of information for the DD platform is the Cal Channel video archive of legislative sessions, a service provided courtesy of cable TV companies that operate in California.

Providing rich narrative assets for games featuring interactive storytelling is both difficult and expensive. Of particular concern to us is the problem of believability for non-player-characters (NPC) in video games and interactive worlds. In addition to art and voice assets which can be substantial, a good NPC requires narrative assets such as universe-appropriate character background, life history, personality, speech mannerisms and behavioral peculiarities.

Comic books are a unique and increasingly popular form of entertainment combining visual and textual elements of communication. This work pertains to making comic books more accessible. Specifically, this paper explains how we detect elements such as speech bubbles present in Japanese comic book panels. Some applications of the work presented in this paper are automatic detection of text and its transformation into audio or into other languages. Automatic detection of elements can also allow reasoning and analysis at a deeper semantic level than what’s possible today.

A method is presented for generating informative and natural-sounding infotips for the graphical elements of a user interface. A domain-specific corpus is prepared using natural language processing techniques and a term-frequency/inverse-document-frequency transform is used for vectorization of features. A k-means clustering algorithm is used to group the corpus by semantic similarity and perform an automatic selection of infotips corresponding to inputted interface labels.

As any veteran of the editor wars can attest, Unix users can be fiercely and irrationally attached to the commands they use and the manner in which they use them. In this work, we investigate the problem of identifying users out of a large set of candidates (25-97) through their command-line histories. Using standard algorithms and feature sets inspired by natural language authorship attribution literature, we demonstrate conclusively that individual users can be identified with a high degree of accuracy through their command-line behavior.