Statistical Dependency Analysis with Support Vector Machines

By Hiroyasu Yamada, Yuji Matsumoto. International Workshop/Conference on Parsing Technologies, 2003

728 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f0e1883cf9d1b3c911125f46359f908557fc5827

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Citations in the corpus

Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020, background): “This specific choice is actually agreed with the YM (Yamada and Matsumoto, 2003) schema.”
Ambiguity-aware Ensemble Training for Semi-supervised Dependency Parsing (2014, methodology): “…directed graph (McDonald et al., 2005; McDonald and Pereira, 2006; Carreras, 2007; Koo and Collins, 2010), while the transition-based method tries to ﬁnd a highest-scoring transition sequence that leads to a legal dependency tree (Yamada and Matsumoto, 2003; Nivre, 2003; Zhang and Nivre, 2011).”
The impact of parse quality on syntactically-informed statistical machine translation (2006, methodology): “The parser is trained on dependencies extracted from the English Penn Treebank version 3.0 (Marcus et al., 1993) by using the head-percolation rules of (Yamada and Matsumoto, 2003).”
Pseudo-Projective Dependency Parsing (2005, methodology): “This is true of the widely used link grammar parser for English (Sleator and Temperley, 1993), which uses a dependency grammar of sorts, the probabilistic dependency parser of Eisner (1996), and more recently proposed deterministic dependency parsers (Yamada and Matsumoto, 2003; Nivre et al., 2004).”
Transition-based Dependency Parsing with Rich Non-local Features (2011, methodology): “Recent research have focused on action sets that build projective dependency trees in anrc-eager (Nivre et al., 2006b; Zhang and Clark, 2008) orarc-standard (Yamada and Matsumoto, 2003; Huang and Sagae, 2010) process.”
A Bayesian Model for Generative Transition-based Dependency Parsing (2015, unknown): “del as a parser on the standard English Penn Treebank (Marcus et al., 1993) setup, training on WSJ sections 02-21, developing on section 22, and testing on section 23. We use the head-ﬁnding rules of Yamada and Matsumoto (2003) (YM)1 for constituencyto-dependency conversion, to enable comparison wit”
An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing (2010, methodology): “The trees were converted to dependency structures with the Penn2Malt conversion program, 6 using the head-ﬁnding rules from (Yamada and Matsumoto, 2003).”
Stack-Pointer Networks for Dependency Parsing (2018, methodology): “, 2007): local and greedy transitionbased algorithms (Yamada and Matsumoto, 2003; Nivre and Scholz, 2004; Zhang and Nivre, 2011; Chen and Manning, 2014), and the globally optimized graph-based algorithms (Eisner, 1996; McDonald et al.”
An Effective Neural Network Model for Graph-based Dependency Parsing (2015, methodology): “We use the English Penn Treebank (PTB) to evaluate our model implementations and Yamada and Matsumoto (2003) head rules are used to extract dependency trees.”
A Neural Probabilistic Structured-Prediction Method for Transition-Based Natural Language Processing (2017, methodology): “For natural language processing tasks such as Chinese word segmentation (Zhang & Clark, 2007), dependency parsing (Yamada & Matsumoto, 2003; Nivre & Scholz, 2004; Zhang & Clark, 2008; Huang & Sagae, 2010; c©2017 AI Access Foundation.”
Advanced Structured Prediction (2014, methodology): “, 1993), converted to a dependency treebank by applying the head rules of Yamada and Matsumoto (2003). For this dataset,”
A Classifier-Based Parser with Linear Run-Time Complexity (2005, methodology): “Two classifier-based deterministic dependency parsers for English have been proposed recently (Nivre and Scholz, 2004; Yamada and Matsumoto, 2003).”
The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies (2008, background): “(Magerman, 1994; Collins, 1999; Yamada and Matsumoto, 2003) is that head–dependent pairs are created from constituents by selecting one word in each phrase as the head and setting all other as its”
Syntactic Processing Using the Generalized Perceptron and Beam Search (2011, methodology): “beam-search to the deterministic model from Yamada and Matsumoto (2003). Row”
Fast and Accurate Shift-Reduce Constituent Parsing (2013, background): “Greedy, classiﬁer-based parsers have been developed for both dependency grammars (Yamada and Matsumoto, 2003; Nivre et al., 2006) and phrase-structure grammars (Sagae and Lavie, 2005).”
Tailoring Continuous Word Representations for Dependency Parsing (2014, unknown): “in our evaluation (Yamada and Matsumoto, 2003; McDonald et al., 2005).”
The CoNLL 2007 Shared Task on Dependency Parsing (2007, methodology): “…shared task, the parsers of Collins (1997) and Charniak (2000), originally developed for English, had been adapted for dependency parsing of Czech, and the parsing methodology proposed by Kudo and Mat-sumoto (2002) and Yamada and Matsumoto (2003) had been evaluated on both Japanese and English.”
Online Learning of Approximate Dependency Parsing Algorithms (2006, methodology): “Yamada and Matsumoto (2003) showed that keeping a small amount of parsing history was crucial to improving parsing performance for their locally-trained shift-reduce SVM parser.”