What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Natural language processing
- Machine learning
Ryan McDonald focuses on Artificial intelligence, Natural language processing, Parsing, Dependency and Dependency grammar.
His work on Statistical model and Joint as part of general Artificial intelligence research is frequently linked to Service, Product and Set, bridging the gap between disciplines.
His Natural language processing research incorporates elements of Segmentation, Labeled data and Data mining.
In his work, S-attributed grammar is strongly intertwined with Machine learning, which is a subfield of Parsing.
His work on Treebank as part of general Dependency research is often related to Transition, thus linking different fields of science.
His Dependency grammar study integrates concerns from other disciplines, such as Top-down parsing, Bottom-up parsing, Algorithm and Parser combinator.
His most cited work include:
- Domain Adaptation with Structural Correspondence Learning (1234 citations)
- Non-Projective Dependency Parsing using Spanning Tree Algorithms (806 citations)
- Online Large-Margin Training of Dependency Parsers (780 citations)
What are the main themes of his work throughout his whole career to date?
Ryan McDonald mainly investigates Artificial intelligence, Natural language processing, Parsing, Machine learning and Dependency grammar.
His research on Artificial intelligence frequently links to adjacent areas such as Speech recognition.
His Natural language processing research is multidisciplinary, incorporating perspectives in Annotation and Information retrieval.
The various areas that Ryan McDonald examines in his Parsing study include Algorithm, Data-driven and Discriminative model.
He combines subjects such as Inference and Zero with his study of Machine learning.
His Dependency grammar study integrates concerns from other disciplines, such as Margin, Theoretical computer science and Bottom-up parsing.
He most often published in these fields:
- Artificial intelligence (74.42%)
- Natural language processing (52.33%)
- Parsing (36.05%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (74.42%)
- Machine learning (27.91%)
- Information retrieval (16.28%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Information retrieval, Natural language processing and Simple.
His Artificial intelligence research incorporates elements of Machine reading and Comprehension.
His work on Relevance as part of his general Machine learning study is frequently connected to Term, thereby bridging the divide between different branches of science.
His Information retrieval research is multidisciplinary, incorporating elements of Correctness and Measure.
His Natural language processing research includes themes of Dependency and Syntax.
Ryan McDonald combines subjects such as Propagation of uncertainty and Data set with his study of Question answering.
Between 2018 and 2021, his most popular works were:
- Zero-shot Neural Retrieval via Domain-targeted Synthetic Query Generation (20 citations)
- RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble. (7 citations)
- BioMRC: A Dataset for Biomedical Machine Reading Comprehension. (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary scientific interests are in Machine learning, Artificial intelligence, Rank, Simple and Search engine.
Ryan McDonald applies his multidisciplinary studies on Machine learning and Quality in his research.
His Quality investigation overlaps with Term, Ranking, Relevance, Zero and Ranking.
Ryan McDonald integrates many fields, such as Rank and Relevance feedback, in his works.
The various areas that Ryan McDonald examines in his Heuristics study include Machine reading, Comprehension and Task.
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.
