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Form-Independent Semantics for Natural Language Understanding

Objective

The project addresses the most important open problem in NLP, to develop a robust semantics that is invariant across different linguistic forms within a language and across languages, and embodies aspects of common-sense knowledge. It will be derived by machine-learning from machine-reading of vast amounts of text, using an existing state-of-the-art wide-coverage CCG semantic parser developed under previous ERC funding to the PI, initially to build traditional semantic analyses of sentences relating named entities.

Patterns of entailment across semantic expressions relating the same named entities will be then detected across other entities of the same types, to construct directed entailment graphs. Cliques within the graphs constitute paraphrases, and can be collapsed to a single cluster identifier. The paraphrase-clustered entailment graph can then be used to redefine the semantics delivered by the parser as conjunctions of entailed paraphrase clusters, to make it invariant under paraphrase and common-sense entailment, yet compatible with a traditional logical operator semantics. The semantics will be extended to a wide range of logical operators, including tense, modality, aspect, and voice, and to implicative and evidential verbs, light verbs, multi-word expressions, and idioms. The method will be applied to semantic parsing, machine translation, knowledge-graph query, and the construction of large knowledge graphs or semantic nets from text, using spreading activation to limit growth in costs of updating and querying the knowledge graph.

In the later stages of the project, the paraphrase-clustered entailment semantics will form the bassi for an incremental semantic parser, using a novel shift-reduce architecture proposed for CCG by the PI in 2000, guided by a modern neural network parsing model acting as a categorial ``supertagger'' and parser action model, for application to language modeling for the machine translation component.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

ERC-ADG - Advanced Grant

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2016-ADG

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Host institution

THE UNIVERSITY OF EDINBURGH
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 996 792,00
Address
OLD COLLEGE, SOUTH BRIDGE
EH8 9YL Edinburgh
United Kingdom

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Region
Scotland Eastern Scotland Edinburgh
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 996 792,00

Beneficiaries (1)

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