Wide-Coverage Semantic Analysis with Boxer

By Johan Bos. Conference on Semantics in Text Processing, 2008

334 citations recorded by the source corpus.

Boxer is an open-domain software component for semantic analysis of text, based on Combinatory Categorial Grammar (CCG) and Discourse Representation Theory (DRT). Used together with the CC (b) discourse structure triggered by conditionals, negation or discourse adverbs was overall correctly computed; (c) some measure and time expressions are correctly analysed, others aren't; (d) several shallow analyses are given for lexical phrases that require deep analysis; (e) bridging references and pronouns are not resolved in most cases. Boxer is distributed with the C&C tools and freely available for research purposes.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5de1e28a1c538142b7c450194cc0a36b4934a45b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae (2014, background): “As opposed to generic tools such as Boxer (Bos 2008) that turn natural language into formal representations, driving the formalization of NL directly by its target ontology ensures that it can be directly consumed by further layers of interpretation, such as reasoners.”
Evaluating Neural Word Representations in Tensor-Based Compositional Settings (2014, methodology): “Given a suitable pairing between words and semantic representations of them, this method can produce structured sentential representations with broad coverage and good generalisability (see e.g. (Bos, 2008)).”
Broad-coverage CCG Semantic Parsing with AMR (2015, methodology): “CCG has also been used for broad-coverage recovery of ﬁrst-order logic representations (Bos, 2008; Lewis and Steedman, 2013).”
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering (2018, background): “In the first range of systems, the Boxer parser (Bos 2008), translates English sentences into first order logic.”
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation (2020, background): “Rule-based semantic parsing systems such as Boxer (Bos, 2008) are able to generalize compo-sitionally by design.”
Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs (2014, methodology): “For obtaining and processing knowledge graphs several related works exist, e.g., the Boxer system (Bos 2008) which is based on Discourse Representation Theory, work by Baral and Dzif-cak (2012) who interpret natural language and inferring unknown annotations using inverse lambda calculus, and the…”
Discourse Representation Structure Parsing (2018, background): “DRS conditions can be basic or complex: Basic conditions express properties of discourse referents or relations between them: ( where (Bos, 2008, 2015), and then reﬁned using expert linguists and crowdsourcing meth-ods.”
A Natural Proof System for Natural Language (2017, methodology): “, 2004), or producing semantics of wide-coverage text in combination with Boxer (Bos et al., 2004; Bos, 2008).”
Injecting Logical Background Knowledge into Embeddings for Relation Extraction (2015, background): “One option is to rely on a fully symbolic approach that exclusively uses first-order logic (Bos and Markert, 2005; Baader et al., 2007; Bos, 2008).”
“Not not bad” is not “bad”: A distributional account of negation (2013, methodology): “This line of research converts logical representations obtained from syntactic parses using Bos’ Boxer (Bos, 2008) into Markov Logic Networks (Richardson and Domingos, 2006), and uses distributional semantics-based models such as that of Erk and Padó (2008) to deal with issues polysemy and ambiguity”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, background): “Many previous hand-engineered natural language understanding systems (Grosz et al., 1987; Alshawi, 1992; Bos, 2008) are designed to build general meaning representations that are adapted for different domains.”
The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity (2014, methodology): “Boxer produces semantic representations based on Discourse Representation Theory (Kamp and Reyle, 1993).”
Broad-Coverage Semantic Parsing as Transduction (2019, background): “…broad-coverage semantic parsing has targeted a number of meaning representation frameworks, including CCG (Steedman, 1996, 2001), DRS (Kamp and Reyle, 1993; Bos, 2008), AMR (Banarescu et al., 2013), UCCA (Abend and Rappoport, 2013), SDP (Oepen et al., 2014, 2015), and UDS (White et al., 2016).”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, background): “A rich and broad literature in MR has approached this problem within a variety of frameworks, including Natural Logic (MacCartney and Manning 2007), Semantic Parsing (Bos 2008), Natural Language Inference and Recognising Textual Entailment (Fyodorov, Winter, and Francez 2000; Bowman et al. 2015), an”
Building a shared world: mapping distributional to model-theoretic semantic spaces (2015, neutral): “The techniques range from completely supervised (Baldwin et al., 2004; Bos, 2008) to lightly supervised (Zettlemoyer and Collins, 2005).”