A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics

Garrette, Dan; Erk, Katrin; Mooney, Raymond J.

doi:10.1007/978-94-007-7284-7_3

Cited by 15 publications

(20 citation statements)

References 19 publications

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“…An alternative approach to compositional DS assumes that the representation of a sentence is not a vector but rather a logical form containing distributional vectors of the content words (Garrette et al 2014, Asher et al 2016, Beltagy et al 2016. The aim is to exploit the complementary strengths of formal semantics and DS.…”

Section: Beyond the Lexicon: Compositional Distributional Semanticsmentioning

confidence: 99%

Distributional Models of Word Meaning

Lenci

2018

Annu. Rev. Linguist.

330

231

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Distributional semantics is a usage-based model of meaning, based on the assumption that the statistical distribution of linguistic items in context plays a key role in characterizing their semantic behavior. Distributional models build semantic representations by extracting co-occurrences from corpora and have become a mainstream research paradigm in computational linguistics. In this review, I present the state of the art in distributional semantics, focusing on its assets and limits as a model of meaning and as a method for semantic analysis.

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Section: Beyond the Lexicon: Compositional Distributional Semanticsmentioning

confidence: 99%

Distributional Models of Word Meaning

Lenci

2018

Annu. Rev. Linguist.

330

231

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“…Such is the difficulty to model negation and other logical terms in DS that Garrette, Erk, and Mooney (2013) have proposed a division of labor between DS, handling lexical relations between content words, and first-order logic, accounting for the semantics of grammatical terms. In this framework, the issue of the distributional meaning of negation does not arise.…”

Section: Negation In Distributional Semanticsmentioning

confidence: 99%

There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics

Kruszewski

Paperno

Bernardi

et al. 2016

Computational Linguistics

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Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a “negated” distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward logical negation. If someone states, in the middle of a conversation, that “This is not a dog,” the negation strongly suggests a restricted set of alternative predicates that might hold true of the object being talked about. In particular, other canids and middle-sized mammals are plausible alternatives, birds are less likely, skyscrapers and other large buildings virtually impossible. Conversational negation acts like a graded similarity function, of the sort that distributional semantics might be good at capturing. In this article, we introduce a large data set of alternative plausibility ratings for conversationally negated nominal predicates, and we show that simple similarity in distributional semantic space provides an excellent fit to subject data. On the one hand, this fills a gap in the literature on conversational negation, proposing distributional semantics as the right tool to make explicit predictions about potential alternatives of negated predicates. On the other hand, the results suggest that negation, when addressed from a broader pragmatic perspective, far from being a nuisance, is an ideal application domain for distributional semantic methods.

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“…Among the most fundamental applications of compositional models are paraphrasing and textual entailment. For instance, by making use of sentence similarity, we should be able to infer that the sentence A stadium craze is sweeping the country entails A craze is covering the nation, but not A craze is brushing the nation (Garrette et al 2014). These applications build compositional vectors from co-occurrences observed in monolingual corpora.…”

Section: Discussionmentioning

confidence: 99%

A dependency-based approach to word contextualization using compositional distributional semantics

Gamallo

2019

JLM

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We propose a strategy to build the distributional meaning of sentences, which is mainly based on two types of semantic objects: context vectors associated with content words and compositional operations driven by syntactic dependencies. The compositional operations of a syntactic dependency make use of two input vectors to build two new compositional vectors representing the contextualized sense of the two related words. Given a sentence, the iterative application of the dependencies results in as many contextualized vectors as content words the sentence contains. At the end of the compositional semantic process, we do not obtain a single compositional vector representing the semantic denotation of the whole sentence (or of the root word), but one contextualized vector for each constituent word of the sentence. Our method avoids the troublesome high-order tensor representations of approaches relying on category theory, by defining all words as first-order tensors (i.e. standard vectors). Some corpus-based experiments are performed to both evaluate the quality of the compositional vectors built with our strategy, and to compare them to other approaches on distributional compositional semantics. The experiments show that our dependency-based compositional method performs as (or even better than) the state-of-the-art.

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A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics

Cited by 15 publications

References 19 publications

Distributional Models of Word Meaning

Distributional Models of Word Meaning

There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics

A dependency-based approach to word contextualization using compositional distributional semantics

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