Natural Language in Multimodal and Multimedia Systems

2012 ◽  
Author(s):  
Elisabeth André
Keyword(s):  
Natural Language ◽  
Rapid Growth ◽  
Multimedia Applications ◽  
Lessons Learned ◽  
Multimedia Systems ◽  
Multimedia Presentation ◽  
Language Technology ◽  
Multimodal Systems ◽  
Explicit Representations ◽  
Elementary Manner

Recent years have witnessed a rapid growth in the development of multimedia applications. Improving technology and tools enable the creation of large multimedia archives and the development of completely new styles of interaction. This article provides a survey of multimedia applications in which natural language plays a significant role. Conventional multimodal systems usually do not maintain explicit representations of the user's input and handle mode integration only in elementary manner. This article shows how the generalization of techniques and representation formalisms developed for the analysis of natural language can help to overcome some of these problems. It surveys techniques for building automated multimedia presentation systems drawing upon lessons learned during the development of natural language generators. Finally, it argues that the integration of natural language technology can lead to a qualitative improvement of existing methods for document classification and analysis.

Multimodal Systems

2014 ◽  
Author(s):  
Elisabeth André ◽  
Jean-Claude Martin
Keyword(s):  
Natural Language ◽  
Rapid Growth ◽  
Large Data ◽  
Access To Information ◽  
Language Technology ◽  
Data Archives ◽  
Multimodal Systems ◽  
Integral Role ◽  
Effective Output

Recent years have witnessed a rapid growth in the development of multimodal systems. Improving technology and tools enable the development of more intuitive styles of interaction and convenient ways of accessing large data archives. Starting from the observation that natural language plays an integral role in many multimodal systems, this chapter focuses on the use of natural language in combination with other modalities, such as body gestures or gaze. It addresses the following three issues: (1) how to integrate multimodal input including spoken or typed language in a synergistic manner; (2) how to combine natural language with other modalities in order to generate more effective output; and (3) how to make use of natural language technology in combination with other modalities in order to enable better access to information.

scholarly journals Natural language processing for web browsing analytics: Challenges, lessons learned, and opportunities

2021 ◽  
pp. 108357
Author(s):  
Daniel Perdices ◽  
Javier Ramos ◽  
José L. García-Dorado ◽  
Iván González ◽  
Jorge E. López de Vergara
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Lessons Learned ◽  
Web Browsing

scholarly journals Lessons Learned from EVALITA 2020 and Thirteen Years of Evaluation of Italian Language Technology

2020 ◽  
Vol 6 (2) ◽  
pp. 79-102
Author(s):  
Lucia C. Passaro ◽  
Maria Di Maro ◽  
Valerio Basile ◽  
Danilo Croce
Keyword(s):  
Lessons Learned ◽  
Italian Language ◽  
Language Technology

Group Synchronization for Multimedia Systems

2019 ◽  
pp. 229-241
Author(s):  
Dimitris N. Kanellopoulos
Keyword(s):  
Multimedia Information ◽  
Multimedia Applications ◽  
Multimedia Systems ◽  
Future Research ◽  
Research Directions ◽  
Media Synchronization ◽  
Future Research Directions ◽  
Group Synchronization

Group or inter-destination media synchronization (IDMS) addresses the presentation of a stream at all the receivers of a group, simultaneously. To ensure synchronized delivery of multimedia information, intelligent synchronization protocols/techniques are required. This chapter illustrates various issues on intra- and inter-media synchronization and presents the basic schemes for inter-destination media synchronization (IDMS). It presents in short IDMS standardization efforts and novel solutions for new multimedia applications. Finally, it outlines future research directions for multimedia group synchronization.

Industry Watch

2007 ◽  
Vol 13 (2) ◽  
pp. 185-189
Author(s):  
ROBERT DALE
Keyword(s):  
Natural Language ◽  
Search Engine ◽  
Question Answering ◽  
University Of California ◽  
News Item ◽  
News Sources ◽  
Language Technology ◽  
Natural Language Question ◽  
The University ◽  
Language Question

“Powerset Hype to Boiling Point”, said a February headline on TechCrunch. In the last installment of this column, I asked whether 2007 would be the year of question-answering. My query was occasioned by a number of new attempts at natural language question-answering that were being promoted in the marketplace as the next advance upon search, and particularly by the buzz around the stealth-mode natural language search company Powerset. That buzz continued with a major news item in the first quarter of this year: in February, Xerox PARC and PowerSet struck a much-anticipated deal whereby PowerSet won exclusive rights to use PARC's natural language technology, as announced in a VentureBeat posting. Following the scoop, other news sources drew the battle lines with titles like “Can natural language search bring down Google?”, “Xerox vs. Google?”, and “Powerset and Xerox PARC team up to beat Google”. An April posting on Barron's Online noted that an analyst at Global Equities Research had cited Powerset in his downgrading of Google from Buy to Neutral. And, all this on the basis of a product which, at the time of writing, very few people have actually seen. Indications are that the search engine is expected to go live by the end of the year, so we have a few more months to wait to see whether this really is a Google-killer. Meanwhile, another question remaining unanswered is what happened to the Powerset engineer who seemed less sure about the technology's capabilities: see the segment at the end of D7TV's PartyCrasher video from the Powerset launch party. For a more confident appraisal of natural language search, check out the podcast of Barney Pell, CEO of Powerset, giving a lecture at the University of California–Berkeley.

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