The application of structured learning in natural language processing
We propose a structured learning approach, max-margin structure (MMS), which is targeted at natural language processing (NLP) tasks. The architecture of our approach is shown to capture structural aspects of the problem domains, leading to demonstrable performance improvements on two NLP tasks: part...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2010-05.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | We propose a structured learning approach, max-margin structure (MMS), which is targeted at natural language processing (NLP) tasks. The architecture of our approach is shown to capture structural aspects of the problem domains, leading to demonstrable performance improvements on two NLP tasks: part-of-speech tagging and statistical machine translation (SMT). We present a perceptron-based online learning algorithm to train the model and demonstrate desirable computational scaling behavior over traditional optimisation methods. |
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