Neural Networks Objective type Questions

Internship: Learning complex textual representations
(multiple choice, type in

Representing text and knowledge

Currently, the representation of knowledge is mainly symbolic, i.e. logical, and is still an area little discussed in the context of numerical and statistical models. However, being able to integrate this knowledge to the representation of objects, so it can be processed by statistical models, is an important issue. The classic example concerns the representation of textual documents which are rarely more complex than simple bags of words. In the case of text, the logical representation is the so-called semantic Web. There are currently no approaches that try to represent “raw” text in the context of a given knowledge. Several solutions can be considered:

  • The first is to represent textual documents directly by creating new nodes and links in the semantic network;
  • The second would be to build a numerical representation of document with respect to a given knowledge In this internship, we will follow the latter approach, since the former might not be able to tackle noisy texts as found on the Web. More precisely, we will develop a numerical model of knowledge from current knowledge resources that can be updated in order to represent the knowledge after reading a document, hence representing a document in the context of a given prior knowledge. The methods will be based on regularized neural networks, and implemented on real data corpora with two target applications:

Source: A quantum world

American Institute of Physics Multiscale Phenomena in Biology: Proceedings of the 2nd Conference on Mathematics and Biology (AIP Conference Proceedings / Mathematical and Statistical Physics)
Book (American Institute of Physics)

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