Daniel Dakota

Topic Modeling Natural Language Processing Techniques Sentiment Analysis and Opinion Mining Speech Recognition and Synthesis Text Readability and Simplification

Publications

Citations

102

Est. group size

Recurring co-author estimate

Active years

Publishing since 2014

Research summary

AI-generated

Daniel Dakota works in natural language processing (NLP), the field of teaching computers to understand and process human language. His research spans syntactic parsing (analyzing sentence grammatical structure), speech emotion recognition, and applications of large language models across multiple languages including English, German, and Swedish. Recent work includes detecting offensive language in social media and examining how prompting techniques affect text generated by AI models.

Syntactic parsing and grammatical structure analysisSpeech emotion recognitionMultilingual and cross-genre NLPLarge language models and prompt engineeringText annotation and linguistic analysis

Publication activity has been variable over the last decade, with notable peaks in 2021 and 2024 and generally steady output averaging about 2.6 papers per year over the past five years.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 2.6/year recently

Recent publications

Performance Gaps in Acted and Naturalistic Speech: Insights from Speech Emotion Recognition Strategies on Customer Service Calls
International conference Recent advances in natural language processing · 2025
Pushing the (Generative) Envelope: Measuring the Effect of Prompt Technique and Temperature on the Generation of Model-based Systems Engineering Artifacts
International conference Recent advances in natural language processing · 2025
All Words and No Play: Identifying Paronomasia in New Kingdom Texts with Pattern Matching
2023
Annotations Matter: Leveraging Multi-task Learning to Parse UD and SUD
2021
Genres, Parsers, and BERT: The Interaction Between Parsers and BERT Models in Cross-Genre Constituency Parsing in English and Swedish
2021
Examining the Effects of Preprocessing on the Detection of Offensive Language in German Tweets
2021
Annotations Matter: Leveraging Multi-task Learning to Parse UD and SUD
Open MIND · 2021
The Devil is in the Details: Parsing Unknown German Words
Lecture notes in computer science · 2018
Brown clustering for unlexicalized parsing
Publication Server of the Institute for German Language (Institute for German Language) · 2016
Acoustic Analysis of high vowels in the Louisiana French of Terrebonne Parish
Indiana Magazine of History (Indiana University) · 2016
Proceedings of the 13th Conference on Natural Language Processing (KONVENS)
Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · 2016

Publishes in

International conference Recent advances in natural language processing×3
Open MIND×2
Natural language processing.×1
Annals of Computer Science and Information Systems×1
Publication Server of the Institute for German Language (Institute for German Language)×1

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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