Parsing with Compositional Vector Grammars

By Richard Socher, John Bauer, Christopher D. Manning, Andrew Y. Ng. 2013

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In Natural Language Processing

Citations in the corpus

Recurrent Neural Network Grammars (2016, background): “First, we must contend with an unbounded number of children, and many previously proposed functions are limited to binary branching nodes (Socher et al., 2013b; Dyer et al., 2015).”
GloVe: Global Vectors for Word Representation (2014, background): “These vectors can be used as features in a variety of applications, such as information retrieval (Manning et al., 2008), document classiﬁcation (Sebastiani, 2002), question answering (Tellex et al., 2003), named entity recognition (Turian et al., 2010), and parsing (Socher et al., 2013).”
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks (2013, background): “Deep learning methods have realized impressive performance in a range of applications, from visual object classification [1, 2, 3] to speech recognition [4] and natural language processing [5, 6].”
Grounded Compositional Semantics for Finding and Describing Images with Sentences (2014, background): “Related work has also been presented at NIPS (Socher et al., 2013b; Frome et al., 2013).”
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models (2015, methodology): “dgeBox method [41]. Following [10,16,20,25], we use 29,783 images for training, 1,000 for validation, and 1,000 for testing. Our split is the same as in [10]. At test time, we use the Stanford parser [33] to identify NP chunks in the test captions, and attempt to localize phrases that exactly match ”
Neural Module Networks (2015, background): “, according to the syntactic structure of the input) [36].”
ReferItGame: Referring to Objects in Photographs of Natural Scenes (2014, methodology): “Parsing the referring expressions: We parse the expressions using the most recent version of the StanfordCoreNLP parser (Socher et al., 2013).”
A Joint Sequence Fusion Model for Video Question Answering and Retrieval (2018, methodology): “[22,23] using recursive neural networks), but they require groundtruth parse trees or segmentation labels.”
Learning to Compose Neural Networks for Question Answering (2016, background): “…the graph transformer networks of Bottou et al. (1997), the knowledge-based neural networks of Towell and Shavlik (1994) and the recursive neural networks of Socher et al. (2013), which use a ﬁxed tree structure to perform further linguistic analysis without any external world representation.”
Exploiting Similarities among Languages for Machine Translation (2013, background): “…and to various other NLP tasks such as word representation learning, named entity recognition, disambiguation, parsing, and tag-w ging (Collobert and Weston, 2008; Turian et al., 2010; Socher et al., 2011; Socher et al., 2013; Col-lobert et al., 2011; Huang et al., 2012; Mikolov et al., 2013a).”
A Fast and Accurate Dependency Parser using Neural Networks (2014, background): “This is inspired by the recent success of distributed word representations in many NLP tasks, e.g., POS tagging (Collobert et al., 2011), machine translation (Devlin et al., 2014), and constituency parsing (Socher et al., 2013).”
Net2Net: Accelerating Learning via Knowledge Transfer (2015, methodology): “Networks with identity weights at initialization have been used before by, for example, Socher et al. (2013), but to our knowledge that have not previously been used to design function-preserving transformations of pre-existing neural networks.”
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (2015, methodology): “These word vectors can be randomly initialized from a uniform distribution (Socher et al., 2013b), or be pre-trained from text corpus with embedding learning algorithms (Mikolov et al., 2013; Pen-nington et al., 2014; Tang et al., 2014).”
A Primer on Neural Network Models for Natural Language Processing (2015, methodology): “Works using the reranking approach include (Socher et al., 2013; Auli et al., 2013; Le & Zuidema, 2014; Zhu et al., 2015a) MEMM and hybrid approaches Other formulations are, of course, also possible.”