An Introduction to Syntactic Analysis and Theory
By H. Koopman, D. Sportiche, E. Stabler. 2013
103 citations recorded by the source corpus.
Subfields: 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 Natural Language Processing
- Samuel R. Bowman
- Mohit Bansal
- Jianfeng Gao
- Adina Williams
- Kyunghyun Cho
- Douwe Kiela
- Yejin Choi
- Luke S. Zettlemoyer
Citations in the corpus
- Neural Network Acceptability Judgments (2018, background): “Wagner et al. (2009) distort real sentences by, for example, deleting words, inserting words, or altering verbal inflection.”
- BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2019, neutral): “We survey several syntax textbooks (e.g., Sag et al., 2003; Adger, 2003; Sportiche et al., 2013), and find that nearly all of the phenomena in BLiMP are discussed in some source.”
- BLiMP: A Benchmark of Linguistic Minimal Pairs for English (2020, unknown): “We survey several syntax textbooks (Sag et al., 2003; Adger, 2003; Sportiche et al., 2013), and find that nearly all of the phenomena in are discussed in some source, and most of the topics that repeatedly appear in textbooks and can be represented with minimal pairs (e.”
- Do latent tree learning models identify meaningful structure in sentences? (2017, background): “Data To ensure that we are able to roughly reproduce the results reported by Yogatama et al. (2017) and Choi et al.”
- Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments (2019, background): “(see Sportiche et al., 2013, pp. 189-193) .”
- Neurophysiological dynamics of phrase-structure building during sentence processing (2017, methodology): “To generate the stimuli, we created a Matlab program rooted in X-bar theory (3, 36) that generated syntactically labeled sentences combining multiple X-bar structures into constituents of variable size, starting from a minimal lexicon (see SI Appendix for details).”
- Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks (2018, background): “, 2015; Schmidhuber, 2015); (ii) to model the compositional effects of language (Koopman et al., 2013; Socher et al., 2013) and help with the long-term dependency problem (Bengio et al.”