Head-Driven Phrase Structure Grammar Parsing on Penn Treebank

By Junru Zhou, Zhao Hai. 2019

155 citations recorded by the source corpus.

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations into head-driven phrase structure. Then two parsing algorithms are respectively proposed for two converted tree representations, division span and joint span. As HPSG encodes both constituent and dependency structure information, the proposed HPSG parsers may be regarded as a sort of joint decoder for both types of structures and thus are evaluated in terms of extracted or converted constituent and dependency parsing trees. Our parser achieves new state-of-the-art performance for both parsing tasks on Penn Treebank (PTB) and Chinese Penn Treebank, verifying the effectiveness of joint learning constituent and dependency structures. In details, we report 95.84 F1 of constituent parsing and 97.00% UAS of dependency parsing on PTB.

Subfields: Artificial Intelligence, Information Extraction, Computational Linguistics & Semantics

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Citations in the corpus

Parsing as Pretraining (2020, methodology): “For constituents, BERT obtains a 93.5% F1-score vs the 95.8% reported by Kitaev and Klein (2018b) and Zhou and Zhao (2019), which are the current best performing models and use BERT representations as input (together with PoS tag embedddings, and in the case of Zhou and Zhao also char embeddings).”
Distilling Neural Networks for Greener and Faster Dependency Parsing (2020, methodology): “43† HPSG (Zhou and Zhao, 2019) 158.”
Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT (2019, result): “Recently, (Zhou and Zhao 2019) achieved impressive results by jointly learning constituency and dependency parsing with BERT.”
Lattice-Based Transformer Encoder for Neural Machine Translation (2019, background): “…to several natural language processing tasks (Li et al., 2018; Zhang et al., 2019; Chen et al., 2018, 2017; Li et al., 2019; He et al., 2018; Zhou and Zhao, 2019), especially to NMT (Bahdanau et al., 2015; Wang et al., 2017a,b, 2018; Wang et al., 2018; Zhang et al., 2018; Zhang and Zhao,…”
SG-Net: Syntax-Guided Machine Reading Comprehension (2019, methodology): “For the syntactic parser, we adopt the dependency parser from Zhou and Zhao (2019) by joint learning of constituent parsing (Kitaev and Klein 2018) using BERT as sole input which achieves very high accuracy: 97.00% UAS and 95.43% LAS on the English dataset Penn Treebank (PTB) (Marcus, Santorini,…”