An Open Source Grammar Development Environment and Broad-coverage English Grammar Using HPSG

By Ann A. Copestake, Dan Flickinger. International Conference on Language Resources and Evaluation, 2000

362 citations recorded by the source corpus.

The LinGO (Linguistic Grammars Online) project’s English Resource Grammar and the LKB grammar development environment are language resources which are freely available for download for any purpose, including commercial use (see http://lingo.stanford.edu). Executable programs and source code are both included. In this paper, we give an outline of the LinGO English grammar and LKB system, and discuss the ways in which they are currently being used. The grammar and processing system can be used independently or combined to give a central component which can be exploited in a variety of ways. Our intention in writing this paper is to encourage more people to use the technology, which supports collaborative development on many levels.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/9605ca18895dc212861244495807f6696655882b

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Citations in the corpus

CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank (2007, methodology): “However, until very recently, only hand-written grammars, which lack the wide coverage and robustness of Treebank parsers, were available for these formalisms (Butt et al. 1999; XTAG-group 1999; Copestake and Flickinger 2000; OpenCCG [White and Baldridge 2003; White 2006]).”
Predicting Grammaticality on an Ordinal Scale (2014, methodology): “Here, we use (1) the Link Grammar Parser 8 and (2) the HPSG English Resource Grammar (Copestake and Flickinger, 2000) and PET parser.”
Syntactic Scaffolds for Semantic Structures (2018, background): “Linguistic theories have argued for a very tight integration of syntactic and semantic processing (Steedman, 2000; Copestake and Flickinger, 2000), and many systems have used syntactic dependency or phrase-based parsers as preprocessing for semantic analysis (Gildea and Palmer, 2002; Punyakanok et a”
Solving logic puzzles: From robust processing to precise semantics (2004, background): “However, grammars that involve precise semantics are still largely hand-built (e.g. ( Carpenter, 1998; Copestake and Flickinger, 2000)).”
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets (2020, methodology): “precision grammar, namely ERG (Copestake and Flickinger, 2000), can be used to automate this process while preserving the authors’ benefit of choosing the perturbations of interest.”
Accuracy vs. Robustness in Grammar Engineering (2010, background): “The English Resource Grammar (ERG: Flickinger, 2000, Flickinger, Copestake, & Sag, 2000, Copestake & Flickinger, 2000) is a broadcoverage grammar which was started in 1994,1 and which has been under continuous development since then within the Linguistic Grammars Online (LinGO) laboratory at CSLI…”
Robust Training under Linguistic Adversity (2017, methodology): “For the deep linguistic parser, we use the LinGO English Resource Grammar (“ERG”: Copestake and Flickinger (2000)) with the ACE parser, based on pyDelphin.”
Multiword Expressions: A Pain in the Neck for NLP (2002, methodology): “An overview of the ERG (from a computational linguistic perspective) is given in Copestake and Flickinger (2000).”
Special Issue Introduction: Semantic Role Labeling: An Introduction to the Special Issue (2008, neutral): “Research has proceeded for decades on manually created lexicons, grammars, and other semantic resources (Hirst 1987; Pustejovsky 1995; Copestake and Flickinger 2000) in support of deep semantic analysis of language input, but such approaches have been labor-intensive and often restricted to narrow…”
Identifying Well-formed Natural Language Questions (2018, background): “Identifying a well-formed question should be easy by parsing with a grammar, such as the English resource grammar (Copestake and Flickinger, 2000), but such grammars are highly precise and fail to parse more than half of web queries.”