Representation and Inference for Natural Language - a First Course in Computational Semantics

By Patrick Blackburn, Johan Bos. CSLI Studies in Computational Linguistics, 2005

313 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6bb3fdcac8220346d712889a0933070b8d32e11f

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Citations in the corpus

Solving logic puzzles: From robust processing to precise semantics (2004, methodology): “We therefore use Discourse Logic (DL), which is SL extended with DRSes and α -expressions as in (Blackburn and Bos, 2000) (which is based on Discourse Representation Theory (Kamp and Reyle, 1993) and its recent extensions for dealing with presuppo-sitions).”
Learning a Compositional Semantic Parser using an Existing Syntactic Parser (2009, background): “This section describes our basic framework, which is based on a fairly standard approach to computational semantics (Blackburn and Bos, 2005).”
Sign-Based Construction Grammar: An Informal Synopsis (2012, unknown): “Blackburn and Bos 2005 provides a useful introduction to the related framework of ' Hole Se - mantics ' , as well as a general discussion of issues in computational semantics .”
Learning executable semantic parsers for natural language understanding (2016, background): “[7] gives a detailed account on how ﬁrst-order logic is used for natural language semantics.”
Wide-Coverage Semantic Analysis with Boxer (2008, methodology): “There is a translation from DRSs to ﬁrst-order formulas, which opens the way to perform inference by including automated reasoning tools such as theorem provers and model builders (Blackburn and Bos, 2005).”
Book Review: Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper (2009, methodology): “It adopts the didactic approach of Blackburn and Bos (2005) and covers ﬁrst-order logic, lambda calculus, Montague-like compositional analysis, and model-based inferencing.”
Vector Space Models of Lexical Meaning (2015, methodology): “One reason is that the formalisms used in the set-theoretic approaches, e.g. first-order predicate calculus, have well-defined inference mechanisms which can be implemented on a computer (Blackburn & Bos, 2005).”
Vector-based Models of Semantic Composition (2008, result): “Our results show that the multiplicative models are superior and correlate significantly with behavioral data.”
A Natural Proof System for Natural Language (2017, background): “The prover also contains a module for the λ-calculus which roughly follows Blackburn and Bos (2005).”
Composition in Distributional Models of Semantics (2010, methodology): “Within symbolic logic, compositionality is accounted for elegantly by assuming a tight correspondence between syntactic expressions and semantic form (Blackburn & Bos, 2005; Montague, 1974).”
Linguistically Motivated Large-Scale NLP with C&C and Boxer (2007, methodology): “This is achieved using the standard translation from DRS to ﬁrst-order logic (Kamp and Reyle, 1993), and allows the output to be pipelined into off-the-shelf theorem provers or model builders for ﬁrst-order logic, to perform consistency or informativeness checking (Blackburn and Bos, 2005).”