Browsing NR vitenarkiv by Author "Lison, Pierre"
Now showing items 1-8 of 8
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Automatic Evaluation of Disclosure Risks of Text Anonymization Methods
Manzanares-Salor, Benet; Sánchez, David; Lison, Pierre (Chapter, 2022)The standard approach to evaluate text anonymization methods consists of comparing their outcomes with the anonymization performed by human experts. The degree of privacy protection attained is then measured with the ... -
Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database
Høst, Anders Mølmen; Lison, Pierre; Moonen, Leon (Chapter, 2023)Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from information ... -
Dialogue Management as Graph Transformations,
Walker, Nicholas Thomas; Dahl, Torbjørn; Lison, Pierre (Chapter, 2022) -
The GDPR and Unstructured Data: Is Anonymisation Possible?
Weitzenboeck, Emily Mary; Lison, Pierre; Cyndecka, Malgorzata Agnieszka; Langford, Malcolm (Journal article; Peer reviewed, 2022)Much of the legal and technical literature on data anonymization has focused on structured data such as tables. However, unstructured data such as text documents or images are far more common, and the legal requirements ... -
Identifying Token-Level Dialectal Features in Social Media
Barnes, Jeremy Claude; Touileb, Samia; Mæhlum, Petter; Lison, Pierre (Chapter, 2023) -
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr; Touileb, Samia (Chapter, 2020)Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques ... -
Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring
Walker, Nicholas Thomas; Ultes, Stephan; Lison, Pierre (Journal article; Peer reviewed, 2023)Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases. This paper presents a novel approach to knowledge-grounded response ... -
skweak: Weak Supervision Made Easy for NLP
Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr (Chapter, 2021)We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machine learning paradigm based on a simple idea: ...