posted by user: rutum || 5605 views || tracked by 15 users: [display]

FAM-LbR/KRAQ 2011 : Learning by Reading and its Applications in Intelligent Question-Answering


When Jul 18, 2011 - Jul 18, 2011
Where Barcelona
Submission Deadline Mar 14, 2011
Notification Due Apr 25, 2011
Final Version Due May 16, 2011
Categories    natural language processing   artificial intelligence   knowledge reasoning

Call For Papers

Joint Workshop FAM-LbR/KRAQ'11
Learning by Reading and its Applications in Intelligent Question-Answering
with IJCAI 2011
July 18, 2011

Call for Papers

It has been a long term dream of AI to develop systems that can emulate human levels of language understanding and reasoning. Recent foundational, methodological and technological developments in Knowledge Representation (e.g. ontologies, knowledge bases incorporating various forms of incompleteness or uncertainty), Reasoning (e.g. data fusion-integration, argumentation, decision theory, fuzzy logic, incomplete knowledge bases, etc.), Natural Language Processing (such as information extraction, relation detection) and formal pragmatics (user models, intentions, etc.) make it possible to foresee the elaboration of much more accurate, cooperative and robust systems dedicated to understanding, learning and answering questions from textual data, operating either on open or closed domains. The time is right to start placing the pieces from all these different areas to develop a unified system for Learning by Reading and Automated Question Answering.

Until now, most approaches for QA and Learning by Reading have been either “narrow and deep” or “broad and shallow”. Many text mining systems embody the latter. An important question arises whether a “broad and deep” approach is a possibility at this stage.

The goal of this workshop is to bring together researchers from different backgrounds (AI, NLP, linguistics, HLT and pragmatics) to explore possibilities of integrating the different techniques for building a system for Learning by Reading and/or Automated Question Answering. The workshop will be focused on models for intelligently analyzing data and cooperatively responding to the user queries. This includes areas such as AI models for processing data coming e.g. from search engines and models that provide users with explanations and arguments about response contents and the way they have been elaborated. Numerous interesting questions arise, including, how can we evaluate such systems automatically or semi-automatically? Is it possible to run such systems on a massive scale? What role does commonsense play in reasoning of textual data? Is it possible to extract this commonsense knowledge automatically?

Topics of interest include (but are not limited to)

* Language processing:
o Analysis of existing language resources such as Wikipedia
o Language analysis (such as question processing, answer identification)
o Language generation and Explanation production
* Reasoning aspects:
o Abductive/deductive, commonsense, and other reasoning
o Reasoning under uncertainty or with incomplete knowledge, models for explanation production and argumentation
o Information fusion-integration,
o Knowledge extraction from text vs. using pre-built knowledge resources
o Bridging knowledge gaps in text through inference
o Knowledge Integration into evolving models
o Bootstrapping Learning
* Pragmatic dimensions of intelligently answering questions:
o User intentions, plans and goals recognition and production
o Conversational implicatures in responses, principles for the design of cooperative systems.
o Learning temporal sequences, causality, and other semantics from text
o Ontology learning, population, or expansion
* Applications:
o Question answering of semi-structured documents such as wikipedia
o Multimedia question answering, where you question a more or less formal representation of the media objects
o Spoken question answering (increasing uncertainty caused by the speech recognition)
* Evaluation:
o Automatic Evaluation of learned Knowledge
o Intrinsic evaluation of inference methods
o Data-intensive vs Knowledge-intensive methods
o Portability techniques for closed domains.

Submission Information

We welcome short papers (max 4 pages), describing projects or ongoing research and long papers (max. 6 pages), that relate more established results. Papers must be sent in .pdf format. The following information MUST be included:

* Title
* Authors' names, affiliations, and email addresses
* Topic(s) of the above list, as appropriate
* Abstract (short summary up to 5 lines)

Important Dates

March 14, 2011 - Paper Submission
April 25, 2011 - Acceptance Notification
May 16, 2011 - Camera ready paper due

FAM-LbR/KRAQ 2011 is held with IJCAI 2011 (July 18, 2011) in downtown Barcelona. Local information can be found from the conference website.

Organizing Co-Chairs

Rutu Mulkar-Mehta (, Patrick Saint-Dizier (
Eduard Hovy (, Marie-Francine Moens (
Bernardo Magnini (
Chris Welty (

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
IJCAI 2022   31st International Joint Conference on Artificial Intelligence
FIEE 2022   MDPI Education Sciences Journal - Future Intelligent Educational Environments: Innovations, Challenges and Applications -- Scopus 2022
AIAPP 2022   9th International Conference on Artificial Intelligence and Applications
MDPI computers 2022   MDPI computers Special Issue on GPU based Applications in Machine Learning - Open for submission
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
ICPR 2022   26th International Conference on Pattern Recognition
ABDAI 2022   2022 4th International Conference on Applications of Big Data and Artificial Intelligence(ABDAI 2022)
Bioinspired Intelligent Algorithms 2021   Special Issue Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications
ECNLPIR 2022   2022 European Conference on Natural Language Processing and Information Retrieval (ECNLPIR 2022)