Learning Semantics

Understanding the semantic structure of unstructured data -- text, dialogs, images -- is a critical challenge given their central role in many applications, including question answering, dialog systems, information retrieval... In recent years, there has been much interest in designing models and algorithms to automatically extract and manipulate these semantic representations from raw data.

Semantics is a diverse field. It encompases extracting structured data from text and dialog data (knowledge base extraction, logical form extraction, information extraction), linguistic approaches to extract and compose representation of meaning, inference and reasoning over meaning representation based on logic or algebra. It also includes approaches that aims at grounding language by learning relations between language and visual observations, linking language to the physical world (e.g. through robotics, machine commands). Despite spanning different disciplines with seemingly incompatible views, these approaches to semantics all aims at enabling computers to evolve and interact with humans and the physical world in general.

The goal of the workshop is dual. First, we aim at gathering experts from the different fields of semantics to favor cross-fertilization, discussions and constructive debates. Second, we encourage invited speakers and participants to expose their future research directions, take position and highlight the key challenges the community need to face. The workshop devotes most of the program to panel sessions about future directions.

** Contributions **

We will welcome contributions (up to 4 pages abstract) in the following areas and related topics:
- Word similarities and sense disambiguation
- Information and relation extraction
- Lexical and compositional semantics
- Learning semantic frames and semantic role labelling
- Grounded language learning
- Semantic representation for dialog understanding
- Visual scene understanding
- Multi-modal semantic representation and reasoning

** Relevant Literature **

Beltagy, I., Chau, C., Boleda, G., Garrette, D., Erk, K., Mooney, R.: Montague Meets Markov: Deep Semantics with Probabilistic Logical Form. Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (*SEM), June 13-14, 2013
Bordes, A., Glorot, X., Weston, J., Bengio., Y.: Joint learning of words and meaning representations for open-text semantic parsing. Proc. of the 17th Intern. Conf. on Artif. Intel. and Stat (2012)
L. Bottou: From machine learning to machine reasoning: an essay,Machine Learning, 94:133-149 (2014)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. Journal of Machine Learning Research 12, 2493–2537 (2011)
Krishnamurthy, J., Mitchell, T.: Vector Space Semantic Parsing: A Framework for Compositional Vector Space Models. Proceedings of the ACL 2013 Workshop on Continuous Vector Space Models and their Compositionality (2013).
Lewis, D.: General semantics. Synthese 22, 18–67 (1970). DOI 10.1007/BF00413598. URL http://dx.doi.org/10.1007/BF00413598
Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. Association for Computational Linguistics (ACL), pp. 590–599 (2011)
Mitchell, J., Lapata, M.: Vector-based models of semantic composition. Proceedings of ACL-08: HLT pp. 236–244 (2008)
Poon, H., Domingos, P.: Unsupervised ontology induction from text. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 296–305. (2010)
Riedel, S., Yao, L., McCallum, A., Marlin, B.: Relation Extraction with Matrix Factorization and Universal Schemas. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2013).
Socher, R., Lin, C.C., Ng, A.Y., Manning, C.D.: Parsing Natural Scenes and Natural Language with Recursive Neural Networks. Proceedings of the 26th International Conference on Machine Learning (ICML) (2011)
Turney, P., Pantel, P.: From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37:141–188 (2010)
Zelle, J., Mooney, R.: Learning to parse database queries using inductive logic programming. Proceedings of the National Conference on Artificial Intelligence (1996)
Zettlemoyer, L., Collins, M.: Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. Proceedings of the Conference on Uncertainty in Artificial Intelligence (2005)
C. L. Zitnick, D. Parikh and L. Vanderwende: Learning the Visual Interpretation of Sentences, International Conference on Computer Vision (2013)