What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His main research concerns Artificial intelligence, Information retrieval, Planner, Information extraction and World Wide Web.
His research integrates issues of Machine learning, Software engineering, Function and Natural language processing in his study of Artificial intelligence.
In his research on the topic of Machine learning, Structure, Knowledge base, Relation and Natural language is strongly related with Data model.
His work deals with themes such as Planning algorithms, Operations research, Representation and Plan, which intersect with Planner.
His Information extraction research includes themes of Precision and recall, Scalability, Set and Pattern recognition.
His research in World Wide Web intersects with topics in Question answering, Application software, Pointwise mutual information and Workflow.
His most cited work include:
- Wrapper induction for information extraction (1018 citations)
- Unsupervised named-entity extraction from the Web: An experimental study (973 citations)
- Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations (721 citations)
What are the main themes of his work throughout his whole career to date?
Daniel S. Weld spends much of his time researching Artificial intelligence, Machine learning, World Wide Web, Information retrieval and Crowdsourcing.
His biological study spans a wide range of topics, including Task and Natural language processing.
He has included themes like Inference and Coreference in his Natural language processing study.
His World Wide Web research focuses on The Internet in particular.
His studies in Crowdsourcing integrate themes in fields like Partially observable Markov decision process, Workflow and Data science.
His study explores the link between Probabilistic logic and topics such as Mathematical optimization that cross with problems in Markov decision process.
He most often published in these fields:
- Artificial intelligence (36.45%)
- Machine learning (16.51%)
- World Wide Web (13.08%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (36.45%)
- Information retrieval (13.40%)
- Machine learning (16.51%)
In recent papers he was focusing on the following fields of study:
Daniel S. Weld focuses on Artificial intelligence, Information retrieval, Machine learning, Task and Data science.
Daniel S. Weld combines subjects such as Generalization, Dialog box and Natural language processing with his study of Artificial intelligence.
The Faceted search research Daniel S. Weld does as part of his general Information retrieval study is frequently linked to other disciplines of science, such as Scientific literature, therefore creating a link between diverse domains of science.
His Machine learning research includes elements of Relationship extraction and Crowdsourcing.
His work carried out in the field of Data science brings together such families of science as Frame, Resource, User interface design, Key and Exploratory search.
As a member of one scientific family, Daniel S. Weld mostly works in the field of Resource, focusing on Aggregate and, on occasion, World Wide Web.
Between 2017 and 2021, his most popular works were:
- SpanBERT: Improving Pre-training by Representing and Predicting Spans (376 citations)
- SpanBERT: Improving Pre-training by Representing and Predicting Spans (376 citations)
- Guidelines for Human-AI Interaction (247 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Daniel S. Weld mainly investigates Artificial intelligence, Data science, Natural language processing, Resource and Task.
He is studying Coreference, which is a component of Artificial intelligence.
His Data science study incorporates themes from Frame, Key and Set.
His work on Sentence and Language model as part of general Natural language processing research is frequently linked to Transformer, thereby connecting diverse disciplines of science.
His Task research integrates issues from Relationship extraction and Machine learning.
His study in the fields of Spurious relationship under the domain of Machine learning overlaps with other disciplines such as Quality.
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.
