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

Download the KMi 2013 Review of the year iBook to your iOS device or alternatively as a PDF.

Journal | 25 years of knowledge acquisition
 

Future Internet is...


Future Internet
With over a billion users, today's Internet is arguably the most successful human artifact ever created. The Internet's physical infrastructure, software, and content now play an integral part of the lives of everyone on the planet, whether they interact with it directly or not. Now nearing its fifth decade, the Internet has shown remarkable resilience and flexibility in the face of ever increasing numbers of users, data volume, and changing usage patterns, but faces growing challenges in meetings the needs of our knowledge society. Globally, many major initiatives are underway to address the need for more scientific research, physical infrastructure investment, better education, and better utilisation of the Internet. Within Japan, USA and Europe major new initiatives have begun in the area.

To succeed the Future Internet will need to address a number of cross-cutting challenges including:

  • Scalability in the face of peer-to-peer traffic, decentralisation, and increased openness

  • Trust when government, medical, financial, personal data are increasingly trusted to the cloud, and middleware will increasingly use dynamic service selection

  • Interoperability of semantic data and metadata, and of services which will be dynamically orchestrated

  • Pervasive usability for users of mobile devices, different languages, cultures and physical abilities

  • Mobility for users who expect a seamless experience across spaces, devices, and velocities