This paper addresses the task of generating descriptions of people for an observer that is moving within a scene. As the observer moves, the descriptions of the people around him also change. A referring expression generation algorithm adapted to this task needs to continuously monitor the changes in the field of view of the observer, his relative position to the people being described, and the relative position of these people to any landmarks around them, and to take these changes into account in the referring expressions generated. This task presents two advantages: many of the mechanisms already available for static contexts may be applied with small adaptations, and it introduces the concept of changing conditions into the task of referring expression generation. In this paper we describe the design of an algorithm that takes these aspects into account in order to create descriptions of people within a 3D virtual environment. The evaluation of this algorithm has shown that, by changing the descriptions in real time according to the observers point of view, they are able to identify the described person quickly and effectively.

This paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional meanings to concepts from the WordNet lexical database, instead of terms, thus allowing to address the word ambiguity problem using one of the many WordNet-based word sense disambiguation algorithms. SentiSense consists of 5,496 words and 2,190 synsets labeled with an emotion from a set of 14 emotional categories, which are related by an antonym relationship. SentiSense has been developed semi-automatically using several semantic relations between synsets in WordNet. SentiSense is endowed with a set of tools that allow users to visualize the lexicon and some statistics about the distribution of synsets and emotions in SentiSense, as well as to easily expand the lexicon. SentiSense is available for research purposes.

This paper presents the process of development and the characteristics of an evaluation collection for a personalisation system for digital newspapers. This system selects, adapts and presents contents according to a user model that define information needs. The collection presented here contains data that are cross-related over four different axes: a set of news items from an electronic newspaper, collected into subsets corresponding to a particular sequence of days, packaged together and cross-indexed with a set of user profiles that represent the particular evolution of interests of a set of real users over the given days, expressed in each case according to four different representation frameworks: newspaper sections, Yahoo categories, keywords, and relevance feedback over the set of news items for the previous day. This information provides a minimum starting material over which one can evaluate for a given system how it addresses the first two observations - adapting to different users and adapting to particular users over time - providing that the particular system implements the representation of information needs according to the four frameworks employed in the collection. This collection has been successfully used to perform some different experiments to determine the effectiveness of the personalization system presented.

Propp's influential structural analysis of fairy tales created a powerful schema for representing storylines in terms of character functions, which is directly exploitable for computational semantic analysis, and procedural generation of stories of this genre. We tackle two resources that draw on the Proppian model - one formalizes it as a semantic markup scheme and the other as an ontology -, both lacking linguistic phenomena explicitly represented in them. The need for integrating linguistic information into structured semantic resources is motivated by the emergence of suitable standards that facilitate this, as well as the benefits such joint representation would create for transdisciplinary research across Digital Humanities, Computational Linguistics, and Artificial Intelligence.