What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Semantic role labeling, Parsing and Syntax.
Daniel Gildea has researched Artificial intelligence in several fields, including Stability and Phonological rule.
As part of his studies on Natural language processing, he frequently links adjacent subjects like Speech recognition.
His Semantic role labeling research incorporates themes from Treebank and Semantic similarity.
His study in Parsing is interdisciplinary in nature, drawing from both Semantics and Graph.
The Syntax study combines topics in areas such as Example-based machine translation, Transfer-based machine translation, Information retrieval and Selection.
His most cited work include:
- The Proposition Bank: An Annotated Corpus of Semantic Roles (1883 citations)
- Automatic labeling of semantic roles (1489 citations)
- Effects of disfluencies, predictability, and utterance position on word form variation in English conversation (303 citations)
What are the main themes of his work throughout his whole career to date?
Daniel Gildea spends much of his time researching Artificial intelligence, Natural language processing, Parsing, Machine translation and Speech recognition.
His Artificial intelligence study incorporates themes from Tree and Machine learning.
His Natural language processing research incorporates elements of Artificial neural network and Embedding.
The various areas that he examines in his Parsing study include Algorithm, Theoretical computer science and Grammar.
His work carried out in the field of Machine translation brings together such families of science as Syntax, Translation and Rule-based machine translation.
His Semantic role labeling study combines topics in areas such as Semantic similarity and Information retrieval.
He most often published in these fields:
- Artificial intelligence (55.70%)
- Natural language processing (44.97%)
- Parsing (31.54%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (55.70%)
- Natural language processing (44.97%)
- Parsing (31.54%)
In recent papers he was focusing on the following fields of study:
Daniel Gildea spends much of his time researching Artificial intelligence, Natural language processing, Parsing, Theoretical computer science and Graph.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Speech recognition and Scripting language.
Bilingual lexicon is the focus of his Natural language processing research.
His work in the fields of Top-down parsing overlaps with other areas such as Baseline.
His Theoretical computer science research is multidisciplinary, relying on both Semantics, Graph neural networks, Computation and Natural language.
His Graph research also works with subjects such as
- Grammar most often made with reference to Text generation,
- Graph that intertwine with fields like Travelling salesman problem and Solver.
Between 2015 and 2021, his most popular works were:
- Leveraging Context Information for Natural Question Generation (81 citations)
- A Graph-to-Sequence Model for AMR-to-Text Generation (78 citations)
- N-ary Relation Extraction using Graph-State LSTM (53 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
His primary scientific interests are in Artificial intelligence, Natural language processing, Graph, Theoretical computer science and Graph.
Daniel Gildea conducts interdisciplinary study in the fields of Artificial intelligence and Meaning through his works.
His work deals with themes such as Machine learning and Variety, which intersect with Natural language processing.
Daniel Gildea combines subjects such as Text generation, Translation, Bottleneck traveling salesman problem, Algorithm and Solver with his study of Graph.
His biological study spans a wide range of topics, including Word, Inference and Coreference.
His Graph study integrates concerns from other disciplines, such as Message passing and Computation.
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