Corpus ID: 8893912

Paraphrase-Driven Learning for Open Question Answering

@inproceedings{Fader2013ParaphraseDrivenLF,
  title={Paraphrase-Driven Learning for Open Question Answering},
  author={Anthony Fader and Luke Zettlemoyer and Oren Etzioni},
  booktitle={ACL},
  year={2013}
}
  • Anthony Fader, Luke Zettlemoyer, Oren Etzioni
  • Published in ACL 2013
  • Computer Science
  • We study question answering as a machine learning problem, and induce a function that maps open-domain questions to queries over a database of web extractions. [...] Key Method Our approach automatically generalizes a seed lexicon and includes a scalable, parallelized perceptron parameter estimation scheme. Experiments show that our approach more than quadruples the recall of the seed lexicon, with only an 8% loss in precision.Expand Abstract
    272 Citations
    Learning to Paraphrase for Question Answering
    • 97
    • PDF
    Enhancing Freebase Question Answering Using Textual Evidence
    • 15
    • PDF
    QUINT: Interpretable Question Answering over Knowledge Bases
    • 17
    • PDF
    Question Answering on Freebase via Relation Extraction and Textual Evidence
    • 165
    • PDF
    Information Extraction over Structured Data: Question Answering with Freebase
    • 340
    • PDF
    Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
    • 29
    • Highly Influenced
    • PDF
    Question Vectors Scores Answer Question q : who created microsoft ?
    Automated Template Generation for Question Answering over Knowledge Graphs
    • 94
    • PDF

    References

    SHOWING 1-10 OF 32 REFERENCES
    An Analysis of the AskMSR Question-Answering System
    • 321
    • PDF
    Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment
    • 493
    • PDF
    Natural Language Questions for the Web of Data
    • 188
    • PDF
    Monolingual Machine Translation for Paraphrase Generation
    • 316
    • PDF
    Learning 5000 Relational Extractors
    • 148
    • PDF
    Template-based question answering over RDF data
    • 418
    • PDF
    Driving Semantic Parsing from the World's Response
    • 225
    • PDF
    Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
    • 755
    • PDF
    Large-scale Semantic Parsing via Schema Matching and Lexicon Extension
    • 231
    • PDF