A Unified Model of Compositional and Distributional Semantics: Theory and Applications
Lead Research Organisation:
University of York
Department Name: Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
HINES P
(2012)
A categorical analogue of the monoid semiring construction
in Mathematical Structures in Computer Science
Hines P
(2013)
AMS review of Amalgams of inverse semigroups and reversible two-counter machines
in AMS mathematical reviews
Hines P
(2013)
AMS review of Lossless quantum data compression and quantum Kolmogorov complexity
in AMS mathematical reviews
Hines P
(2013)
AMS review of On synchronized multi-tape and multi-head automata
in AMS mathematical reviews
Hines P
(2016)
Coherence and strictification for self-similarity
in Journal of Homotopy and Related Structures
Veluru S
(2014)
Correlated community estimation models over a set of names
Komninos A
(2016)
Dependency Based Embeddings for Sentence Classification Tasks
Klapaftis I
(2013)
Evaluating Word Sense Induction and Disambiguation Methods
in Language Resources and Evaluation
Description | The mathematics behind models of language, logic, quantum physics, and computation have a common core. Transferring tools from one field to another allows us to extend models of meaning beyond simple noun phrases, to include words with logical or structural meanings. The application of tools from theoretical and quantum computing to models of language, developed within the project, has resulted in drastic simplification of the complexity of using these models. These simplifications also have useful applications in other fields of computer science and mathematics. |
Exploitation Route | The findings provide the mathematical and logical machinery for researchers and programmers seeking more comprehensive tools to analyze natural language, and provide a route to making such tools significantly more efficient and less computationally costly. They also demonstrate that other proposed approaches cannot be fruitful for fundamental structural reasons. |
Sectors | Digital/Communication/Information Technologies (including Software) |
URL | http://arxiv.org/abs/1303.3170 |
Description | Knowledge Transfer Partnership |
Amount | £470,000 (GBP) |
Funding ID | KTP010852 |
Organisation | Innovate UK |
Sector | Public |
Country | United Kingdom |
Start | 04/2018 |
End | 04/2021 |
Description | FET Open EU Grant Proposal |
Organisation | National University of Distance Education |
Country | Spain |
Sector | Academic/University |
PI Contribution | We have intiated joint research with UNED to continue the work undertaken in the current project. We have submitted an FET Open grant proposal which has been possible directly as a result of the current project. |
Collaborator Contribution | They have contributed to the writing of the FET Open grant proposal |
Impact | None yet |
Start Year | 2014 |
Description | FET Open EU Grant Proposal |
Organisation | University of the Basque Country |
Country | Spain |
Sector | Academic/University |
PI Contribution | We have intiated joint research with UNED to continue the work undertaken in the current project. We have submitted an FET Open grant proposal which has been possible directly as a result of the current project. |
Collaborator Contribution | They have contributed to the writing of the FET Open grant proposal |
Impact | None yet |
Start Year | 2014 |
Title | Dependency based embeddings |
Description | We provide dependency based word embeddings and related code for a skipgram variant word embedding that utilizes additional information from dependency graphs. This can be employed in a broad range of natural language processing applications such sentiment analysis, question answering and information retrieval. |
Type Of Technology | Software |
Year Produced | 2016 |
Open Source License? | Yes |
Impact | We also provide experimental results that show that dependency based embeddings can outperform standard window based embeddings in many natural language processing tasks. Specifically, for three different classification methods: a Support Vector Machine, a Convolutional Neural Network and a Long Short Term Memory Network; the use of our dependency based embeddings improve on question classification, sentiment analysis and relation classification tasks. |
URL | http://www.cs.york.ac.uk/nlp/extvec |