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Classroom Contents
Language Learning in Humans and Machines - Making Connections to Make Progress
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- 1 Intro
- 2 Today's talk
- 3 Interpreting a sentence
- 4 The real situation
- 5 Other examples of ambiguity
- 6 Labelled examples
- 7 Extracting features
- 8 Statistical natural language processing
- 9 Labelled data is hard to obtain
- 10 Result: unequal access
- 11 Human language learning
- 12 Must computers learn like humans?
- 13 But language isn't "in the world"
- 14 Research programme
- 15 Learning biases
- 16 Stronger bias = less data
- 17 Example problem: segmentation
- 18 Word segmentation
- 19 Statistical learning experiment
- 20 Testing for learning
- 21 How do they do it?
- 22 What about real language?
- 23 Another strategy
- 24 A model for segmenting words
- 25 The right bias can help
- 26 The Dirichlet process model
- 27 Output of the system
- 28 Words aren't marbles
- 29 Improved system
- 30 Where else can these ideas help?
- 31 Continuing work
- 32 Meaning as translation
- 33 Results so far
- 34 Conclusions
- 35 Acknowledgements
