Logical Inference on Dependency-based Compositional Semantics

Tian, Ran; Miyao, Yusuke; Matsuzaki, Takashi

doi:10.3115/v1/p14-1008

Cited by 27 publications

(29 citation statements)

References 28 publications

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“…The experimental results on the FraCaS test suite have shown that a reasonable number of lexical entries and non-first-order axioms enable various logical inferences in an efficient way and outperform the state-of-the-art first-order system. Future work will focus on incorporating a robust model of lexical knowledge (Lewis and Steedman, 2013;Tian et al, 2014) to our framework.…”

Section: Resultsmentioning

confidence: 99%

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Higher-order logical inference with compositional semantics

Mineshima¹,

Martínez-Gómez²,

Miyao³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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We present a higher-order inference system based on a formal compositional semantics and the wide-coverage CCG parser. We develop an improved method to bridge between the parser and semantic composition. The system is evaluated on the FraCaS test suite. In contrast to the widely held view that higher-order logic is unsuitable for efficient logical inferences, the results show that a system based on a reasonably-sized semantic lexicon and a manageable number of non-first-order axioms enables efficient logical inferences, including those concerned with generalized quantifiers and intensional operators, and outperforms the state-of-the-art firstorder inference system.

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Section: Resultsmentioning

confidence: 99%

“…We are aware of two other systems tested on FraCaS that are capable of multiple-premise inferences: the CCG-based first-order system of Lewis and Steedman (2013) and the dependency-based compositional semantics of Tian et al (2014). These systems were only evaluated on the Quantifier section of FraCaS.…”

Section: Methodsmentioning

confidence: 99%

Higher-order logical inference with compositional semantics

Mineshima¹,

Martínez-Gómez²,

Miyao³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Self Cite

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show abstract

“…Labels on the two ends of an edge, initially indicating fields of tables in a database, are considered as semantic roles of the corresponding words. Abstract denotation is proposed to capture the meaning of this abstract version of DCS tree, and a textual inference system based on abstract denotation is built (Tian et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…3 Alignment with logical clues Tian et al (2014) proposed a way to generate onthe-fly knowledge to fill knowledge gaps: if H is not proven, compare DCS trees of T and H to generate path alignments (e.g. blamed for death ∼ cause loss of life, as underscored in Figure 1); evaluate the path alignments by a similarity score function; and path alignments with a score greater than a threshold (0.4) are accepted and converted to inference rules.…”

Section: Introductionmentioning

confidence: 99%

“…( §3) With rigorous inference on DCS trees, where does logic contribute in the system of Tian et al (2014) In the tree transformation based approach to RTE, it has been realized that some gaps between T and H cannot be filled even by a large number of tree transformation rules extracted from corpus (BarHaim et al, 2007a). For example in Figure 1, it is possible to extract the rule blamed for death → cause loss of life, but not easy to extract tropical storm Debby → storm, because "Debby" could be an arbitrary name which may not even appear in the corpus.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the ACL 2014 Workshop on Semantic Parsing

Lewis¹,

Steedman²

2014

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While there has been significant recent work on learning semantic parsers for specific task/ domains, the results don't transfer from one domain to another domains. We describe a project to learn a broad-coverage semantic lexicon for domain independent semantic parsing. The technique involves several bootstrapping steps starting from a semantic parser based on a modest-sized hand-built semantic lexicon. We demonstrate that the approach shows promise in building a semantic lexicon on the scale of WordNet, with more coverage and detail that currently available in widely-used resources such as VerbNet. We view having such a lexicon as a necessary prerequisite for any attempt at attaining broad-coverage semantic parsing in any domain. The approach we described applies to all word classes, but in this paper we focus here on verbs, which are the most critical phenomena facing semantic parsing. Introduction and MotivationRecently we have seen an explosion of work on learning semantic parsers (e.g., Matuszek, et al, 2012;Tellex et al, 2013; Branavan et al, 2010, Chen et al, 2011). While such work shows promise, the results are highly domain dependent and useful only for that domain. One cannot, for instance, reuse a lexical entry learned in one robotic domain in another robotic domain, let alone in a database query domain. Furthermore, the techniques being developed require domains that are simple enough so that the semantic models can be produced, either by hand or induced from the application. Language in general, however, involves much more complex concepts and connections, including discussion of involves abstract concepts, such as plans, theories, political views, and so on. It is not clear how the techniques currently being developed could be generalized to such language.The challenge we are addressing is learning a broad-coverage, domain-independent semantic parser, i.e., a semantic parser that can be used in any domain. At present, there is a tradeoff between the depth of semantic representation produced and the coverage of the techniques. One of the critical gaps in enabling more general, deeper semantic systems is the lack of any broadcoverage deep semantic lexicon. Such a lexicon must contain at least the following information: i. an enumeration of the set of distinct senses for the word (e.g., as in WordNet, PropBank), linked into an ontology that supports reasoning ii. For each sense, we would have• Deep argument structure, i.e., semantic roles with selectional preferences • Constructions that map syntax to the deep argument structure (a.k.a. linking rules) • Lexical entailments that characterize the temporal consequences of the event described by the verb The closest example to such lexical entries can be found in VerbNet (Kipper et al, 2008), a handbuilt resource widely used for a range of general applications. An example entry from VerbNet is seen in Figure 1, which describes a class of verbs called murder-42.1. VerbNet clusters verbs by the constructions they take, not by sense or meaning, alt...

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QNP Textual Entailment with Polynomial Event Semantics

Kiselyov

Watanabe

2023

Lecture Notes in Computer Science

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Logical Inference on Dependency-based Compositional Semantics

Cited by 27 publications

References 28 publications

Higher-order logical inference with compositional semantics

Higher-order logical inference with compositional semantics

Proceedings of the ACL 2014 Workshop on Semantic Parsing

QNP Textual Entailment with Polynomial Event Semantics

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