An adaptive contextual quantum language model
- Submitting institution
-
The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1460281
- Type
- D - Journal article
- DOI
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10.1016/j.physa.2016.03.003
- Title of journal
- Physica A: Statistical Mechanics and its Applications
- Article number
- -
- First page
- 51
- Volume
- 456
- Issue
- -
- ISSN
- 0378-4371
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- 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
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, published in an interdisciplinary journal, was the first adaptive quantum language model (QLM) that captures the dynamic information need within a user search session within a density matrix transformation framework. It has been successfully applied and evaluated with a Microsoft Bing search log dataset. It is regarded as one of the representative works for QLM and the density matrix representation of information (instead of the traditional vector representation), and such representation has been adopted in other application areas including Twitter sentiment analysis (Zhang et al., 2018) and click-through rate production (Niu and Hou, 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -