Probabilistic Models for Learning a Semantic Parser Lexicon

@inproceedings{Krishnamurthy2016ProbabilisticMF,
  title={Probabilistic Models for Learning a Semantic Parser Lexicon},
  author={Jayant Krishnamurthy},
  booktitle={HLT-NAACL},
  year={2016}
}
We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the accuracy and efficiency of the final semantic parser. Existing work on lexicon learning has focused on heuristic methods that lack convergence guarantees and require significant human input in the form of lexicon templates or annotated logical… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • An experimental evaluation on a set of 4th grade science questions demonstrates that our models improve semantic parser accuracy (35-70% error reduction) and efficiency (4-25x more sentences per second) relative to prior work despite using less human input.
  • Our models improve semantic parser accuracy (35-70% error reduction) over prior work despite using less human input.

References

Publications referenced by this paper.
SHOWING 1-10 OF 33 REFERENCES

Learning Dependency-Based Compositional Semantics

  • Computational Linguistics
  • 2011
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL