Search Results for author:
Found 9 papers, 3 papers with code
Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis
The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information.
Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
This difference is particularly marked for communicative feedback and grounding phenomena such as backchannels, acknowledgments, or clarification requests.
Bootstrapping Text Anonymization Models with Distant Supervision
Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals.
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods.
Building a Norwegian Lexical Resource for Medical Entity Recognition
We present a large Norwegian lexical resource of categorized medical terms.
Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation
We present a framework and its implementation relying on Natural Language Processing methods, which aims at the identification of exercise item candidates from corpora.
Detecting Context Dependence in Exercise Item Candidates Selected from Corpora
We explore the factors influencing the dependence of single sentences on their larger textual context in order to automatically identify candidate sentences for language learning exercises from corpora which are presentable in isolation.
SweLL on the rise: Swedish Learner Language corpus for European Reference Level studies
Inter-rater agreement is presented on the basis of SW1203 subcorpus.
A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity
Corpora and web texts can become a rich language learning resource if we have a means of assessing whether they are linguistically appropriate for learners at a given proficiency level.
