What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Linguistics
- Programming language
His primary scientific interests are in Artificial intelligence, Natural language processing, Linguistics, Bigram and Syntax.
Frank Keller has included themes like Machine learning and Computer vision in his Artificial intelligence study.
His Natural language processing research incorporates themes from Language production and Structural priming, Priming.
His works in Grammar and Locality are all subjects of inquiry into Linguistics.
His Bigram study combines topics in areas such as Web application, World Wide Web, Range, Semantics and Syntax.
His Syntax research focuses on subjects like Grammaticality, which are linked to Focus.
His most cited work include:
- Using the web to obtain frequencies for unseen bigrams (317 citations)
- Data from Eye-Tracking Corpora as Evidence for Theories of Syntactic Processing Complexity. (290 citations)
- Image Description using Visual Dependency Representations (210 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Natural language processing, Linguistics, Sentence and Parsing.
Specifically, his work in Artificial intelligence is concerned with the study of Image.
His Natural language processing study combines topics from a wide range of disciplines, such as Probabilistic logic, Verb and German.
His work deals with themes such as Psycholinguistics and Priming, which intersect with Linguistics.
His study in Sentence is interdisciplinary in nature, drawing from both Language production and Cognitive psychology.
His Reading research includes elements of Speech recognition and Eye tracking.
He most often published in these fields:
- Artificial intelligence (59.05%)
- Natural language processing (47.62%)
- Linguistics (23.33%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (59.05%)
- Natural language processing (47.62%)
- Reading (10.48%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Reading, Image and Verb.
Frank Keller combines subjects such as Narrative structure and Machine learning with his study of Artificial intelligence.
His biological study spans a wide range of topics, including Context, Narrative and Image retrieval.
His research in Reading focuses on subjects like Speech recognition, which are connected to Part-of-speech tagging, Part of speech, Eye movement, Memorization and Question answering.
His research in Image intersects with topics in Ranking, Representation, Matching, Similarity and Ranking.
Frank Keller has researched Verb in several fields, including Dependency graph, Predictive modelling, Statistical classification and Human eye.
Between 2015 and 2021, his most popular works were:
- Automatic description generation from images: a survey of models, datasets, and evaluation measures (184 citations)
- Extreme Clicking for Efficient Object Annotation (123 citations)
- We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification (75 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Programming language
His primary areas of study are Artificial intelligence, Natural language processing, Pascal, Computer vision and Image.
The various areas that Frank Keller examines in his Artificial intelligence study include Machine learning, German and Identification.
While working in this field, Frank Keller studies both Natural language processing and Screenwriting.
His Pascal course of study focuses on Object and Image segmentation, Initialization and Factor.
His Image research is multidisciplinary, relying on both Range, Word-sense disambiguation, Verb and Action.
Parsing and Syntax is closely connected to Language model in his research, which is encompassed under the umbrella topic of Reinforcement learning.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
