What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Natural language processing
Udo Hahn spends much of his time researching Artificial intelligence, Natural language processing, Information retrieval, Annotation and Data science.
His Artificial intelligence study incorporates themes from Structure, Coherence and German.
His work deals with themes such as Domain, Terminology and Anaphora, which intersect with Natural language processing.
Udo Hahn has included themes like Complement, Knowledge extraction, Domain knowledge and Knowledge base in his Information retrieval study.
He combines subjects such as Emotion classification, Categorical variable, Scheme, Named-entity recognition and Data set with his study of Annotation.
His Data science study combines topics from a wide range of disciplines, such as Knowledge representation and reasoning, Text mining, Constraint, Key and Automatic summarization.
His most cited work include:
- A systems study reveals concurrent activation of AMPK and mTOR by amino acids (319 citations)
- The challenges of automatic summarization (267 citations)
- Functional centering: grounding referential coherence in information structure (156 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Natural language processing, Information retrieval, German and Annotation are his primary areas of study.
His studies in Domain knowledge, Description logic, Lexicon, Text corpus and Knowledge representation and reasoning are all subfields of Artificial intelligence research.
His Natural language processing study frequently links to related topics such as Knowledge base.
His Knowledge base research focuses on Knowledge-based systems and how it relates to Knowledge extraction.
His work in Information retrieval addresses subjects such as Text mining, which are connected to disciplines such as Data science.
His work in Parsing tackles topics such as Grammar which are related to areas like Dependency.
He most often published in these fields:
- Artificial intelligence (62.01%)
- Natural language processing (56.17%)
- Information retrieval (22.40%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (62.01%)
- Natural language processing (56.17%)
- German (13.31%)
In recent papers he was focusing on the following fields of study:
Udo Hahn spends much of his time researching Artificial intelligence, Natural language processing, German, Word and Information retrieval.
The various areas that Udo Hahn examines in his Artificial intelligence study include Domain, Machine learning and Task.
The Lexicon research Udo Hahn does as part of his general Natural language processing study is frequently linked to other disciplines of science, such as Quality, therefore creating a link between diverse domains of science.
The study incorporates disciplines such as Scheme, Unified Medical Language System, Text corpus and Terminology in addition to German.
His Word research includes themes of Contrast, Stability, Meaning, Connotation and Reliability.
His Information retrieval research includes elements of Parallel corpora and Machine translation.
Between 2013 and 2021, his most popular works were:
- A systems study reveals concurrent activation of AMPK and mTOR by amino acids (319 citations)
- EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis (80 citations)
- Bad Company—Neighborhoods in Neural Embedding Spaces Considered Harmful (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Natural language processing
Udo Hahn mostly deals with Artificial intelligence, Natural language processing, Word, German and Information retrieval.
His biological study spans a wide range of topics, including Domain, Task, Data science, Considered harmful and Open software.
His work in the fields of F1 score overlaps with other areas such as Protected health information.
His research in Word intersects with topics in Reliability, Speech recognition, Emotion classification and Word meaning.
His studies in German integrate themes in fields like Data anonymization and Analytics.
His research integrates issues of World Wide Web and Distribution in his study of Information retrieval.
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