SIGAI Workshop on Emerging Research Trends
in Artificial Intelligence (ERTAI - 2010)
17th April, 2010, C-DAC, Navi Mumbai, India
Supported by Computer Society of India (CSI)
Keynote Address by Prof. Rajeev Sangal, IIIT Hyderabad
Two-stage Constraint Parsing for Indian Languages
Natural Language Processing deals with understanding and developing computational theories of human language. Such theories allow us to understand the structure of language and build computer software that can process language. For example, if a query in a human language can be processed (that is, analyzed and understood) by the machine, then it can try to find an answer from a given database or from a set of documents. A search engine of the future is likely to use such a technology.
Parsing gives the grammatical analysis of a given sentence. Here, we will describe 2-stage parsing in the Computational Paninian Grammar framework. The parser is a constraint solver, where constraints are expressed in the form of integer programming constraints. Research results regarding its performance would be presented, and compared with data driven parsing.
Natural Language Processing deals with understanding and developing computational theories of human language. Such theories allow us to understand the structure of language and build computer software that can process language. For example, if a query in a human language can be processed (that is, analyzed and understood) by the machine, then it can try to find an answer from a given database or from a set of documents. A search engine of the future is likely to use such a technology.
Parsing gives the grammatical analysis of a given sentence. Here, we will describe 2-stage parsing in the Computational Paninian Grammar framework. The parser is a constraint solver, where constraints are expressed in the form of integer programming constraints. Research results regarding its performance would be presented, and compared with data driven parsing.
Invited Talk by Dr. R Uthurusamy, General Motors
AI Research Trends and Resources: A Personal View
A personal view of current AI Research Trends and Resources will be presented in three parts. First part will outline available resources for AI researchers and practitioners and resources on advice for beginning graduates on doing research. Second part consists of short videos of a select set of AI and other innovative research projects. The concluding part will present a few actionable suggestions to assist those seeking interesting AI research areas and innovative applications.
A personal view of current AI Research Trends and Resources will be presented in three parts. First part will outline available resources for AI researchers and practitioners and resources on advice for beginning graduates on doing research. Second part consists of short videos of a select set of AI and other innovative research projects. The concluding part will present a few actionable suggestions to assist those seeking interesting AI research areas and innovative applications.
Invited Talk by Dr. Hiranmay Ghosh, TCS Innovation Labs, Delhi
Semantic Multimedia Web
The vision of semantic web proposes an environment where the data and services on the web can be semantically interpreted and processed by machines to facilitate human consumption. In today's cyberspace, audio-visual artifacts compete with traditional text and data in their information content. Machine interpretation of multimedia data is therefore essential for realization of the semantic web vision. Semantic web technology relies on ontology as a tool for modeling an abstract view of the real world and contextual semantic analysis of documents. Ontology languages like Web Ontology Language (OWL) uses linguistic constructs for modeling the real-world and can be conveniently used for interpreting textual documents. An attempt to use ontology for interpreting multimedia contents is hindered by the semantic gap that exists between media features appearing in the documents and the linguistic structures representing the concepts in the ontology. We argue that the concepts have their roots in perceptual experience of human beings and the apparent disconnect between the conceptual and the perceptual worlds is rather artificial. The key to semantic processing of media data lays in harmonizing the seemingly isolated conceptual and the perceptual worlds. In this context, we propose a new ontology based approach for contextual semantic interpretation of multimedia data and services on the web. This ontology representation “Multimedia Web Ontology Language (MOWL)” is an extension of OWL and supports perceptual modeling and reasoning essential for semantic multimedia applications.
The vision of semantic web proposes an environment where the data and services on the web can be semantically interpreted and processed by machines to facilitate human consumption. In today's cyberspace, audio-visual artifacts compete with traditional text and data in their information content. Machine interpretation of multimedia data is therefore essential for realization of the semantic web vision. Semantic web technology relies on ontology as a tool for modeling an abstract view of the real world and contextual semantic analysis of documents. Ontology languages like Web Ontology Language (OWL) uses linguistic constructs for modeling the real-world and can be conveniently used for interpreting textual documents. An attempt to use ontology for interpreting multimedia contents is hindered by the semantic gap that exists between media features appearing in the documents and the linguistic structures representing the concepts in the ontology. We argue that the concepts have their roots in perceptual experience of human beings and the apparent disconnect between the conceptual and the perceptual worlds is rather artificial. The key to semantic processing of media data lays in harmonizing the seemingly isolated conceptual and the perceptual worlds. In this context, we propose a new ontology based approach for contextual semantic interpretation of multimedia data and services on the web. This ontology representation “Multimedia Web Ontology Language (MOWL)” is an extension of OWL and supports perceptual modeling and reasoning essential for semantic multimedia applications.
And Research paper presentations along with Open discussion on AI Research Trends, Challenges & Methodologies
For more information please visit: http://sigai.cdacmumbai.in/index.php/ertai-2010
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