What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Natural language processing, Parsing, Word and Inference.
The Artificial intelligence study combines topics in areas such as Machine learning, Task and Set.
His work carried out in the field of Natural language processing brings together such families of science as Annotation and FrameNet.
His studies in Parsing integrate themes in fields like Dependency, Representation and Graph.
His Word study combines topics in areas such as WordNet, Space, Binary number and Embedding.
His biological study spans a wide range of topics, including Commonsense reasoning and Translation.
His most cited work include:
- From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (1444 citations)
- Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments (812 citations)
- Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters (599 citations)
What are the main themes of his work throughout his whole career to date?
Noah A. Smith mostly deals with Artificial intelligence, Natural language processing, Machine learning, Parsing and Task.
His Word, Language model, Dependency grammar, Inference and Natural language investigations are all subjects of Artificial intelligence research.
Noah A. Smith regularly ties together related areas like Variety in his Language model studies.
As part of his studies on Natural language processing, he frequently links adjacent subjects like Context.
His specific area of interest is Machine learning, where he studies Structured prediction.
Noah A. Smith has included themes like Dependency, Probabilistic logic and Theoretical computer science in his Parsing study.
He most often published in these fields:
- Artificial intelligence (67.20%)
- Natural language processing (46.24%)
- Machine learning (18.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.20%)
- Natural language processing (46.24%)
- Language model (12.07%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Natural language processing, Language model, Machine learning and Task are his primary areas of study.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Code.
His research in Natural language processing intersects with topics in Word and Vocabulary.
His Word research incorporates themes from Range and Coreference.
The study incorporates disciplines such as Domain, Semantics, Transformer and Machine translation in addition to Language model.
His studies in Machine learning integrate themes in fields like Test data, Variety and State.
Between 2018 and 2021, his most popular works were:
- Linguistic Knowledge and Transferability of Contextual Representations (335 citations)
- Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (225 citations)
- Knowledge Enhanced Contextual Word Representations (185 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Noah A. Smith focuses on Artificial intelligence, Machine learning, Language model, Natural language processing and Word.
Noah A. Smith has researched Artificial intelligence in several fields, including Test data, Task and Set.
His studies deal with areas such as Data mapping, Benchmark, State and Code as well as Machine learning.
His studies examine the connections between Language model and genetics, as well as such issues in Transformer, with regards to Recurrent neural network and Computer engineering.
His Natural language processing study incorporates themes from Similarity and Coreference.
His work carried out in the field of Word brings together such families of science as Polyglot, Semantic similarity, Range, Dependency grammar and Reinforcement learning.
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