What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Information retrieval, Artificial intelligence, Plagiarism detection, Natural language processing and World Wide Web.
His Information retrieval research incorporates elements of Data mining, Information needs, Classifier, Web page and Fingerprint.
His studies in Artificial intelligence integrate themes in fields like Stability and Machine learning.
His studies deal with areas such as Text alignment, Field, Internet privacy and Identification as well as Plagiarism detection.
His study in Natural language processing is interdisciplinary in nature, drawing from both Class imbalance, Argumentation theory, Process and Robustness.
His research investigates the link between World Wide Web and topics such as Profiling that cross with problems in Gender and Language and Library science.
His most cited work include:
- Overview of the 2nd International Competition on Plagiarism Detection (295 citations)
- An Evaluation Framework for Plagiarism Detection (248 citations)
- A Stylometric Inquiry into Hyperpartisan and Fake News (196 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Information retrieval, Artificial intelligence, Task, World Wide Web and Natural language processing.
His Information retrieval research focuses on Search engine, Web search query, Plagiarism detection, Query expansion and Relevance.
His Artificial intelligence research focuses on subjects like Argumentation theory, which are linked to Argument.
His World Wide Web study combines topics from a wide range of disciplines, such as Identification, Software and Profiling.
In his study, he carries out multidisciplinary Profiling and Digital text research.
His research on Natural language processing frequently connects to adjacent areas such as Argumentative.
He most often published in these fields:
- Information retrieval (37.28%)
- Artificial intelligence (25.74%)
- Task (18.93%)
What were the highlights of his more recent work (between 2018-2021)?
- Information retrieval (37.28%)
- Task (18.93%)
- Artificial intelligence (25.74%)
In recent papers he was focusing on the following fields of study:
Benno Stein focuses on Information retrieval, Task, Artificial intelligence, Argument and World Wide Web.
Many of his research projects under Information retrieval are closely connected to Benchmarking with Benchmarking, tying the diverse disciplines of science together.
The Task study combines topics in areas such as Cognitive psychology and Information needs.
Benno Stein has included themes like Identification, Machine learning and Natural language processing in his Artificial intelligence study.
His Natural language processing study integrates concerns from other disciplines, such as Domain and Annotation.
His World Wide Web research includes themes of Conversational search, Change detection and Profiling.
Between 2018 and 2021, his most popular works were:
- TIRA Integrated Research Architecture. (98 citations)
- Data Acquisition for Argument Search: The args.me Corpus (20 citations)
- A Decade of Shared Tasks in Digital Text Forensics at PAN (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Task, Information retrieval, Argument, Natural language processing and Artificial intelligence are his primary areas of study.
His Task research integrates issues from Authorship attribution, Cognitive science, Style and Change detection.
His studies deal with areas such as Software and World Wide Web as well as Authorship attribution.
His study in Information retrieval is interdisciplinary in nature, drawing from both State and Profiling.
His research in Argument intersects with topics in Cognitive psychology, Set, Argumentation theory, Rhetorical question and Argumentative.
His Natural language processing research is multidisciplinary, incorporating elements of Domain, Writing style and Recall.
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