KMi Seminars
Learning Conditional Random Fields from Unaligned Data for Natural Language Understanding
This event took place on Friday 28 October 2011 at 11:30

 
Dr. Deyu Zhou School of Computer Science and Engineering, Southeast University, China

One of the key tasks in natural language understanding is semantic parsing which maps natural language sentences to complete formal meaning representations. Rule-based approaches are typically domain-specific and often fragile. Statistical approaches are able to accommodate the variations found in real data and hence can in principle be more robust. However, statistical approaches need fully annotated data for training the models. A learning approach to train conditional random fields from unaligned data for natural language understanding is proposed and discussed. The learning approach resembles the expectation maximization algorithm. It has two advantages, one is that only abstract annotations are needed instead of fully word-level annotations, and the other is that the proposed learning framework can be easily extended for training other discriminative models, such as support vector machines, from abstract annotations. The proposed approach has been tested on the DARPA Communicator Data. Experimental results show that it outperforms the hidden vector state (HVS) model, a modified hidden Markov model also trained on abstract annotations.

 
KMi Seminars
KMi 2013 - A review of the year

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Journal | 25 years of knowledge acquisition
 

Semantic Web and Knowledge Services is...


Semantic Web and Knowledge Services
"The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation" (Berners-Lee et al., 2001).

Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...

Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.