Abstract
Modelling compositional meaning for sentences using empirical distributional methods has been a challenge for computational linguists. We implement the abstract categorical model of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) using data from the BNC and evaluate it. The implementation is based on unsupervised learning of matrices for relational words and applying them to the vectors of their arguments. The evaluation is based on the word disambiguation task developed by Mitchell and Lapata (2008) for intransitive sentences, and on a similar new experiment designed for transitive sentences. Our model matches the results of its competitors in the first experiment, and betters them in the second. The general improvement in results with increase in syntactic complexity showcases the compositional power of our model.
Keywords
Categorical variableComputer scienceTransitive relationNatural language processingArtificial intelligenceTask (project management)Meaning (existential)Word (group theory)AnimacyLinguisticsMachine learningMathematicsPsychology
Affiliated Institutions
Related Publications
Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics
Speakers of a language can construct an unlimited number of new words through morphological derivation. This is a major cause of data sparseness for corpus-based approaches to l...
Estimating Linear Models for Compositional Distributional Semantics
In distributional semantics studies, there is a growing attention in compositionally determining the distributional meaning of word sequences. Yet, compositional distributional ...
Composition in Distributional Models of Semantics
Abstract Vector‐based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply ...
A SICK cure for the evaluation of compositional distributional semantic models
Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional ...
Distributional Memory: A General Framework for Corpus-Based Semantics
Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tac...
Publication Info
- Year
- 2011
- Type
- article
- Pages
- 1394-1404
- Citations
- 223
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
223
OpenAlex
Cite This
Edward Grefenstette,
Mehrnoosh Sadrzadeh
(2011).
Experimental Support for a Categorical Compositional Distributional Model of Meaning.
arXiv (Cornell University)
, 1394-1404.
https://doi.org/10.48550/arxiv.1106.4058
Identifiers
- DOI
- 10.48550/arxiv.1106.4058