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Text Chunking using Transformation-Based Learning

About Chunking

Conclusions

  • Focuses on identifying low-level noun groups by using hand-built grammars and finite state techniques
  • Hidden Markov Models (HMM) statistical tool for modeling a wide range of time series data.
  • Introduce as a tagging problem,able to apply transformation-based learning
  • Some transformation-based learning algorithms are useful in other settings

Future Directions

Introduction

Transformational Text Chunking

Transformation-Based Learning Mechanism

  • Different paths to increase systems power
  • Diminish linguistic contexts
  • Adding templates to chunk structure
  • Involves dividing sentences into non overlapping segments on the basis of fairly superficial analysis.
  • Provides a foundation for further levels of analysis such as: verb-argument identification.
  • Brill 1993
  • Sequence of transformational rules i learned, which improves a baseline model for interpretative text
  • Part of speech tagging
  • Prepositional phrase attachment
  • Assigning unlabeled binary-branching tree sentences
  • Can be encoded as a tagging problem
  • Different Encoding Choices
  • A baseline system to go off of
  • Rule template to follow

Algorithm Design Issues

Results

  • Organization of the computation
  • Indexing Static Rule Elements
  • Heuristic Disabling of unlikely rules
  • Derivation of training and testing data
  • Analysis of Initial Rules
  • Contribution of Lexical Templates
  • Frequent Error Classes
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