A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 225618847
- Type
- D - Journal article
- DOI
-
10.1016/j.csl.2017.04.010
- Title of journal
- Computer Speech and Language
- Article number
- -
- First page
- 113
- Volume
- 46
- Issue
- -
- ISSN
- 0885-2308
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
8
- Research group(s)
-
B - Data Science
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaborative work, which involved four universities and the Oxford University Press (OUP), developed a major system for analysing meaning from language data. The system combines advanced Natural Language Processing (NLP) techniques/tools and the largest Historical Thesaurus of English in the world. Published in a top NLP journal “Computer Speech and Language”, this work has had a major impact on the area of corpus-based NLP. The system has been used to generate large corpora for NLP, and the resources were used by OUP to improve their dictionaries and were explored in a project to develop an advanced information search system.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
