What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
Jonathan Berant mainly investigates Artificial intelligence, Natural language processing, Parsing, Reading comprehension and Machine learning.
His work in the fields of Recurrent neural network and Top-down parsing overlaps with other areas such as Research community, Speedup and Database schema.
His work on Natural language understanding, Predicate and Language model is typically connected to Process as part of general Natural language processing study, connecting several disciplines of science.
His Parsing research integrates issues from Principle of compositionality, Knowledge base, Programmer, Structured prediction and Lisp.
The study incorporates disciplines such as Question answering, Selection and Focus in addition to Reading comprehension.
His Machine learning study frequently draws parallels with other fields, such as Interpreter.
His most cited work include:
- Semantic Parsing on Freebase from Question-Answer Pairs (1042 citations)
- Semantic Parsing via Paraphrasing (426 citations)
- Building a Semantic Parser Overnight (259 citations)
What are the main themes of his work throughout his whole career to date?
Jonathan Berant mostly deals with Artificial intelligence, Natural language processing, Parsing, Machine learning and Question answering.
As a part of the same scientific family, Jonathan Berant mostly works in the field of Artificial intelligence, focusing on Crowdsourcing and, on occasion, Formal language.
The concepts of his Natural language processing study are interwoven with issues in Range, Logical consequence, Reading comprehension and Benchmark.
His research in Parsing intersects with topics in Representation, Structured prediction, SQL and Logical form.
His work deals with themes such as Feature engineering, Interpreter and Lisp, which intersect with Artificial neural network.
His research integrates issues of Stability, Pruning and Programmer in his study of Feature engineering.
He most often published in these fields:
- Artificial intelligence (88.73%)
- Natural language processing (50.70%)
- Parsing (44.37%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (88.73%)
- Natural language processing (50.70%)
- Language model (17.61%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Language model, Parsing and Question answering.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
His work in the fields of Natural language processing, such as Transliteration, intersects with other areas such as Glyph.
Jonathan Berant combines subjects such as Simple and Transformer with his study of Language model.
His Parsing study integrates concerns from other disciplines, such as Tree and Test set.
His Question answering research focuses on Crowdsourcing and how it connects with Formal language, Metric and Information retrieval.
Between 2019 and 2021, his most popular works were:
- Evaluating NLP Models via Contrast Sets (47 citations)
- Break It Down: A Question Understanding Benchmark (32 citations)
- Break It Down: A Question Understanding Benchmark (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
His primary areas of investigation include Artificial intelligence, Natural language processing, Language model, Parsing and Theoretical computer science.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Crowdsourcing and Machine learning.
His work on Question answering as part of general Natural language processing study is frequently connected to Learning curve, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Language model research is multidisciplinary, incorporating perspectives in Range, Conjunction, Protocol and Composition.
His studies in Parsing integrate themes in fields like Contrast, Decision boundary, Test data, Supervised learning and Decision rule.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Artificial neural network, Principle of compositionality, Reading comprehension and Forcing.
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