Francisco Iacobelli

Ethnic Identity and Engagement in Embodied Conversational Agents

In this paper we present the design, development and initial evaluation of a virtual peer that models ethnicity through culturally authentic verbal and non-verbal behaviors. The behaviors chosen for the implementation come from an ethnographic study with African-American and Caucasian children and the evaluation of the virtual peer consists of a study in which children interacted with an African American or a Caucasian virtual peer and then assessed its ethnicity. Results suggest that it may be possible to tip the ethnicity of a embodied conversational agent by changing verbal and non-verbal behaviors instead of surface attributes, and that children engage with those virtual peers in ways that have promise for educational applications.

Tell me more, not just "more of the same"

The Web makes it possible for news readers to learn more about virtually any story that interests them. Media outlets and search engines typically augment their information with links to similar stories. It is up to the user to determine what new information is added by them, if any. In this paper we present Tell Me More, a system that performs this task automatically: given a seed news story, it mines the web for similar stories reported by different sources and selects snippets of text from those stories which offer new information beyond the seed story. New content may be classified as supplying: additional quotes, additional actors, additional figures and additional information depending on the criteria used to select it. In this paper we describe how the system identifies new and informative content with respect to a news story. We also how that providing an explicit categorization of new information is more useful than a binary classification (new/not-new). Lastly, we show encouraging results from a preliminary evaluation of the system that validates our approach and encourages further study.

Information Finding with Robust Entity Detection: The Case of an Online News Reader

Journalists and editors work under tight deadlines and are forced to gather as much background and details as they can about a particular situation or event. They have to keep track of useful sources and they have to be able to record what aspects and what portions of the source provided useful information.

Semi-Supervised Approach to Named Entity Recognition in Spanish Applied to a Real-World Conversational System

In this paper, we improve the named-entity recognition NER capabilities for an already existing text-based dialog system TDS in Spanish. Our solution is twofold: first, we developed a hidden Markov model part-of-speech POS tagger trained with the frequencies from over 120-million words; second, we obtained 2,i¾?283 real-world conversations from the interactions between users and a TDS. All interactions occurred through a natural-language text-based chat interface. The TDS was designed to help users decide which product from a well-defined catalog best suited their needs. The conversations were manually tagged using the classical Penn Treebank tag set, with the addition of an ENTITY tag for all words relating to a brand or product. The proposed system uses an hybrid approach to NER: first it looks up each word in a previously defined catalog. If the word is not found, then it uses the tagger to tag it with its appropriate POS tag. When tested on an independent conversation set, our solution presented a higher accuracy and higher recall rates compared to a current development from the industry.