Copertina

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Meisenbacher, Stephen, Nestorov, Svetlozar, Norlander, Peter
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computers and Society Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2026)

Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective
di: Vladika, Juraj, et al.
Pubblicazione: (2024)

Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models
di: Ahnert, Georg, et al.
Pubblicazione: (2024)

Applying BioBERT to Extract Germline Gene-Disease Associations for Building a Knowledge Graph from the Biomedical Literature
di: Gonzalez, Armando D. Diaz, et al.
Pubblicazione: (2023)

WAXAL-NET: Finetuned Edge ASR Across 19 African Languages
di: Olufemi, Victor Tolulope, et al.
Pubblicazione: (2026)

With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2025)

Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2024)

Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2024)

Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus
di: Litterer, Benjamin, et al.
Pubblicazione: (2024)

LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2025)

Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce
di: Ousidhoum, Nedjma, et al.
Pubblicazione: (2024)

Towards Better Inclusivity: A Diverse Tweet Corpus of English Varieties
di: Pham, Nhi, et al.
Pubblicazione: (2024)

The Moral Foundations Reddit Corpus
di: Trager, Jackson, et al.
Pubblicazione: (2022)

SPOT: An Annotated French Corpus and Benchmark for Detecting Critical Interventions in Online Conversations
di: Berriche, Manon, et al.
Pubblicazione: (2025)

Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators
di: Zhu, Hongyin
Pubblicazione: (2023)

The Cambridge Law Corpus: A Dataset for Legal AI Research
di: Östling, Andreas, et al.
Pubblicazione: (2023)

Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting
di: Fillies, Jan, et al.
Pubblicazione: (2025)

Corpus-Based Approaches to Igbo Diacritic Restoration
di: Ezeani, Ignatius
Pubblicazione: (2026)

ARCADE: A City-Scale Corpus for Fine-Grained Arabic Dialect Tagging
di: Nacar, Omer, et al.
Pubblicazione: (2026)

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
di: Hua, Yining, et al.
Pubblicazione: (2022)

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2025)

Lexical Substitution is not Synonym Substitution: On the Importance of Producing Contextually Relevant Word Substitutes
di: Vladika, Juraj, et al.
Pubblicazione: (2025)

On the Impact of Noise in Differentially Private Text Rewriting
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2025)

A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2026)

A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2024)

1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy
di: Meisenbacher, Stephen, et al.
Pubblicazione: (2024)

A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product
di: Basak, Madhusudan, et al.
Pubblicazione: (2024)

Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements
di: Karamolegkou, Antonia, et al.
Pubblicazione: (2024)

Computational Studies in Influencer Marketing: A Systematic Literature Review
di: Gui, Haoyang, et al.
Pubblicazione: (2025)

Mapping Violence: Developing an Extensive Framework to Build a Bangla Sectarian Expression Dataset from Social Media Interactions
di: Tasnim, Nazia, et al.
Pubblicazione: (2024)

From Text to Talent: A Pipeline for Extracting Insights from Candidate Profiles
di: Frazzetto, Paolo, et al.
Pubblicazione: (2025)

Extracting memorized pieces of (copyrighted) books from open-weight language models
di: Cooper, A. Feder, et al.
Pubblicazione: (2025)

Towards Equitable AI: Detecting Bias in Using Large Language Models for Marketing
di: Yilmaz, Berk, et al.
Pubblicazione: (2025)

REInstruct: Building Instruction Data from Unlabeled Corpus
di: Chen, Shu, et al.
Pubblicazione: (2024)

Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publications
di: Filimonovic, Dragan, et al.
Pubblicazione: (2025)

Sovereign AI-based Public Services are Viable and Affordable
di: Branco, António, et al.
Pubblicazione: (2026)

Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing
di: Gui, Haoyang, et al.
Pubblicazione: (2025)

LLM-Generated Feedback Supports Learning If Learners Choose to Use It
di: Thomas, Danielle R., et al.
Pubblicazione: (2025)

Dual Use Concerns of Generative AI and Large Language Models
di: Grinbaum, Alexei, et al.
Pubblicazione: (2023)

Measuring the Gap Between Media Coverage and Public Information Demand: Evidence from the 2026 Lebanon Conflict
di: Soufan, Mohamed
Pubblicazione: (2026)