Robust garden path parsing
By Brian Roark. 2004
30 citations recorded by the source corpus.
This paper presents modifications to a standard probabilistic context-free grammar that enable a predictive parser to avoid garden pathing without resorting to any ad-hoc heuristic repair. The resulting parser is shown to apply efficiently to both newspaper text and telephone conversations with complete coverage and excellent accuracy. The distribution over trees is peaked enough to allow the parser to find parses efficiently, even with the much larger search space resulting from overgeneration. Empirical results are provided for both Wall St. Journal and Switchboard test corpora.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Noah A. Smith
- Chris Dyer
- Yoav Goldberg
- Graham Neubig
- Miguel Ballesteros
- A. Kuncoro
- Mohit Bansal
- Luke S. Zettlemoyer
In Natural Language Processing
- Chris Dyer
- Noah A. Smith
- Miguel Ballesteros
- Graham Neubig
- Yoav Goldberg
- A. Kuncoro
- Phil Blunsom
- Mirella Lapata
Citations in the corpus
- Recurrent Neural Network Grammars (2016, background): “It is likewise closely related to the “linearized” parse trees proposed by Vinyals et al. (2015) and to the top-down, left-to-right decompositions of trees used in previous generative parsing and language modeling work (Roark, 2001, 2004; Charniak, 2010).”
- Supervised and unsupervised PCFG adaptation to novel domains (2003, methodology): “Bacchiani and Roark (2003) investigated MAP adaptation of n-gram language models, in a way that is straight-forwardly applicable to probabilistic context-free grammars (PCFGs).”