• Corpus ID: 10585087

Automatic Domain Adaptation for Parsing

@inproceedings{McClosky2010AutomaticDA,
  title={Automatic Domain Adaptation for Parsing},
  author={David McClosky and Eugene Charniak and Mark Johnson},
  booktitle={NAACL},
  year={2010}
}
Current statistical parsers tend to perform well only on their training domain and nearby genres. While strong performance on a few related domains is sufficient for many situations, it is advantageous for parsers to be able to generalize to a wide variety of domains. When parsing document collections involving heterogeneous domains (e.g. the web), the optimal parsing model for each document is typically not obvious. We study this problem as a new task --- multiple source parser adaptation. Our… 

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References

SHOWING 1-10 OF 35 REFERENCES
The Domain Dependence of Parsing
TLDR
Comparison of structure distributions across domains; examples of domain specific structures; and Parsing experiment using some domain dependent grammars demonstrate domain dependence and idiosyncrasy of syntactic structure.
Reranking and Self-Training for Parser Adaptation
TLDR
The reranking parser described in Charniak and Johnson (2005) improves performance of the parser on Brown to 85.2% and use of the self-training techniques described in (McClosky et al., 2006) raise this to 87.8% (an error reduction of 28%) again without any use of labeled Brown data.
Learning Reliability of Parses for Domain Adaptation of Dependency Parsing
TLDR
The goal was to improve the performance of a state-of-the-art dependency parser on the data set of the domain adaptation track of the CoNLL 2007 shared task, a formidable challenge.
Parser Evaluation and the BNC: Evaluating 4 constituency parsers with 3 metrics
TLDR
This work evaluates discriminative parse reranking and parser self-training on a new English test set using four versions of the Charniak parser and a variety of parser evaluation metrics and finds that reranking leads to a performance improvement on the new test set (albeit a modest one).
Subdomain Sensitive Statistical Parsing using Raw Corpora
TLDR
This paper presents a method that exploits raw subdomain corpora gathered from the web to introduce subdomain sensitivity into a given parser, and employs statistical techniques for creating an ensemble of domain sensitive parsers, and explores methods for amalgamating their predictions.
Corpus Variation and Parser Performance
TLDR
This work examines how other types of text might a ect parser performance, and how portable parsing models are across corpora by comparing results for the Brown and WSJ corpora, and considers which parts of the parser's probability model are particularly tuned to the corpus on which it was trained.
Automatic Prediction of Parser Accuracy
TLDR
This paper proposes a technique that automatically takes into account certain characteristics of the domains of interest, and accurately predicts parser performance on data from these new domains, and has a cheap and effective recipe for measuring the performance of a statistical parser on any given domain.
An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing
TLDR
The effectiveness of the proposed methods on dependency parsing experiments using two widely used test collections: the Penn Treebank for English, and the Prague Dependency Tree-bank for Czech are demonstrated.
Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking
TLDR
This paper describes a simple yet novel method for constructing sets of 50- best parses based on a coarse-to-fine generative parser that generates 50-best lists that are of substantially higher quality than previously obtainable.
Head-Driven Statistical Models for Natural Language Parsing
  • M. Collins
  • Computer Science
    Computational Linguistics
  • 2003
TLDR
Three statistical models for natural language parsing are described, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree.
...
...