Introduction to the special issue on learning semantics

A key ambition of AI is to render computers able to evolve and interact with the real world.This can be made possible only if the machine is able to produce an interpretation of its avail-able modalities (image, audio, text, etc.) which can be used to support reasoning and takingappropriate actions. Computational linguists use the term

[1]  Dan Roth,et al.  Confidence Driven Unsupervised Semantic Parsing , 2011, ACL.

[2]  Pedro M. Domingos,et al.  Unsupervised Ontology Induction from Text , 2010, ACL.

[3]  Luke S. Zettlemoyer,et al.  A Joint Model of Language and Perception for Grounded Attribute Learning , 2012, ICML.

[4]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Raymond J. Mooney,et al.  Learning to Parse Database Queries Using Inductive Logic Programming , 1996, AAAI/IAAI, Vol. 2.

[6]  Rohit J. Kate,et al.  Learning Language Semantics from Ambiguous Supervision , 2007, AAAI.

[7]  Raymond J. Mooney,et al.  Panning for gold: finding relevant semantic content for grounded language learning , 2011, MLSLP.

[8]  Dan Klein,et al.  Learning Dependency-Based Compositional Semantics , 2011, CL.

[9]  David A. Forsyth,et al.  Describing objects by their attributes , 2009, CVPR.

[10]  Jason Weston,et al.  Towards Understanding Situated Natural Language , 2010, AISTATS.

[11]  Ben Taskar,et al.  Learning from ambiguously labeled images , 2009, CVPR.

[12]  Pedro F. Felzenszwalb Object detection grammars , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[13]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[14]  Jason Weston,et al.  Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing , 2012, AISTATS.

[15]  Luke S. Zettlemoyer,et al.  Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars , 2005, UAI.

[16]  Ming-Wei Chang,et al.  Driving Semantic Parsing from the World’s Response , 2010, CoNLL.

[17]  B. Taskar,et al.  Learning from ambiguously labeled images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  E. Doerr,et al.  General Semantics. , 1958, Science.

[19]  Mirella Lapata,et al.  Vector-based Models of Semantic Composition , 2008, ACL.

[20]  Richard M. Schwartz,et al.  A Fully Statistical Approach to Natural Language Interfaces , 1996, ACL.

[21]  Luke S. Zettlemoyer,et al.  Bootstrapping Semantic Parsers from Conversations , 2011, EMNLP.

[22]  Alexei A. Efros,et al.  Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics , 2010, ECCV.

[23]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[24]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.