Dependency Parsing

By Hung-yi Lee, Danqi Chen, Christopher D. Manning, Christopher Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, Noah A. Smith, D. Andor, Chris Alberti and 10 more. Encyclopedia of Artificial Intelligence, 2009

624 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/25202a549e76a50473b6608ba160b82e916c1c34

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Citations in the corpus

Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations (2016, methodology): “rsing, using recent techniques from the neural-networks (“deep learning”) literature. Modern approaches to dependency parsing can be broadly categorized into graph-based and transition-based parsers (Kübler et al., 2008). Graph-based parsers (McDonald, 2006) treat parsing as a search-based structure”
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020, background): “We note that UAS has been shown to be highly sensitive to annotation variations (Schwartz et al., 2011; Tsarfaty et al., 2011; K¨ubler et al., 2009).”
Inducing Syntactic Trees from BERT Representations (2019, background): “a head of a syntactic construction determines its syntactic category and can often replace it without any damage of syntactic correctness (Kübler et al., 2009; Lopatková et al., 2005).”
Universal Dependency Annotation for Multilingual Parsing (2013, background): “Common divergences are found in the 1Downloadable at https://code.google.com/p/uni-dep-tb/. analysis of coordination, verb groups, subordinate clauses, and multi-word expressions (Nilsson et al., 2007; Kübler et al., 2009; Zeman et al., 2012).”
Improving Arabic Dependency Parsing with Lexical and Inflectional Morphological Features (2010, methodology): “Hence MaltParser features are de-ﬁned as POS tag at top of the stack, word form at top of the buffer, etc. Kübler et al. (2009) describe a “typical” MaltParser model conﬁguration of attributes and features.”
A Natural Proof System for Natural Language (2017, background): “Before our work, natural logic in NLP community was understood as a mode of shallow reasoning which is based on phrase substitutions and is limited to single-premised arguments.”
Survey on reinforcement learning for language processing (2021, methodology): “In the specific application of dependency parsing [59], it has been shown that a parser can be implemented to use a policy learned by reinforcement learning, in order to select the optimal transition in each parsing stage [137].”
Scene Graph Parsing as Dependency Parsing (2018, background): “The goal of dependency parsing (K¨ubler et al., 2009) is to assign a parent word to every word in a sentence, and every such connection is associated with a label.”
Easy-First Dependency Parsing with Hierarchical Tree LSTMs (2016, background): “e parser’s implementation is available for download at the ﬁrst author’s webpage. 1 Introduction Dependency-based syntactic representations of sentences are central to many language processing tasks (Kübler et al., 2008). Dependency parse-trees encode not only the syntactic structure of a sentence b”
Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers (2013, methodology): “Dependency parsing has become a prominent approach to syntax in the last few years, with increasingly fast and accurate models being devised (K¨ubler et al., 2009; Huang and Sagae, 2010; Zhang and Nivre, 2011; Rush and Petrov, 2012).”
A Fast and Accurate Dependency Parser using Neural Networks (2014, background): “In recent years, enormous parsing success has been achieved by the use of feature-based discriminative dependency parsers (Kübler et al., 2009).”
Grammatical structures for word-level sentiment detection (2012, positive): “We now describe how we build the syntactic relatedness trie (SRT) that forms the scaffolding for the probabilistic models needed to identify sentiment-bearing words via syntactic constraints extracted from a dependency parse (Kübler et al., 2009).”