Monday, July 13, 2009

Jaime Arguello -- Thursday July 16th, 2009, 11:00 AM

Speaker: Jaime Arguello (Language Technologies Institute, School of Computer Science, Carnegie Mellon University)

Time/Date: Thursday July 16th, 2009, 11:00 AM.

Place: Wean Hall 7220

Title: Sources of Evidence for Vertical Selection

Web search providers often include search services for domain-specific subcollections, called verticals, such as news, images, videos, job postings, company summaries, and artist profiles. We address the problem of vertical selection, predicting relevant verticals (if any) for queries issued to a search engine's main web search page. In contrast to prior collection selection tasks, vertical selection is associated with unique resources that can inform the classification decision. We focus on three sources of evidence: (1) the query string, from which features are derived independent of external resources, (2) logs of queries previously issued to the vertical directly by users, and (3) corpora representative of vertical content. These sources of evidence are integrated as features in a classification-based approach. We make use of and compare against prior work in federated search and retrieval effectiveness prediction. Our evaluation focuses on 18 different verticals, which differ in terms of semantics, media type, size, and level of query traffic. An in-depth error analysis reveals unique challenges across different verticals and provides insight into vertical selection for future work.

Based on work conducted at Yahoo! Labs Montreal to be presented at SIGIR 2009.

Tuesday, May 26, 2009

Kai-min Kevin Chang -- Friday May 29, 2009, noon

Speaker: Kai-min Kevin Chang (Language Technologies Institute, School of Computer Science, Carnegie Mellon University)

Time/Date: Friday May 29, 2009, noon.

Place: NSH 1507 (Note the room is not the usual NSH 3002)

Title: Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation

Abstract: Recent advances in functional Magnetic Resonance Imaging (fMRI) offer a significant new approach to studying semantic representations in humans by making it possible to directly observe brain activity while people comprehend words and sentences. In this study, we investigate how humans comprehend adjective-noun phrases (e.g. strong dog) while their neural activity is recorded. Classification analysis shows that the distributed pattern of neural activity contains sufficient signal to decode differences among phrases. Furthermore, vector-based semantic models can explain a significant portion of systematic variance in the observed neural activity. Multiplicative composition models of the two-word phrase outperform additive models, consistent with the assumption that people use adjectives to modify the meaning of the noun, rather than conjoining the meaning of the adjective and noun.

This talk is based on the author's ACL 2009 paper.

Monday, May 11, 2009

Hua Ai -- Friday May 15, 2009, noon

Speaker: Hua Ai (Intelligent Systems Program at University of Pittsburgh)

Date/Time: Friday May 15, 2009, noon
Location: 3002 Newell-Simon Hall (NSH)

User Simulation for Spoken Dialog System Development

In this talk, I will present my thesis study on investigating how to
evaluate and how to build user simulations to help dialog system
development. When evaluating user simulations, I use both human judges and
automatic evaluation measures to assess the simulation model qualities.
When building user simulations, I examine three factors that impact
simulation models in the tasks of dialog strategy learning and dialog
system development.

The talk is based on the author's ACL 2009 paper.