What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Artificial intelligence, Natural language processing, Automatic summarization, Information retrieval and Task are her primary areas of study.
The concepts of her Artificial intelligence study are interwoven with issues in Machine learning, Baseline and Set.
She has included themes like Context and Word in her Natural language processing study.
Her research integrates issues of Document retrieval, Data mining and Cluster analysis in her study of Automatic summarization.
Her work carried out in the field of Information retrieval brings together such families of science as Transcription and Segmentation.
Her research in Task intersects with topics in Speech recognition, Arabic, Semitic languages and Hebrew.
Her most cited work include:
- Using lexical chains for text summarization (843 citations)
- Modeling local coherence: An entity-based approach (499 citations)
- Modeling local coherence: An entity-based approach (499 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Machine learning, Task and Theoretical computer science.
Her Artificial intelligence research focuses on Set and how it connects with Data mining.
The study incorporates disciplines such as Structure, Speech recognition and Information retrieval in addition to Natural language processing.
Her Machine learning study combines topics in areas such as Representation and Inference.
The various areas that Regina Barzilay examines in her Task study include Question answering, Contrast, Baseline and Reinforcement learning.
Her Theoretical computer science study integrates concerns from other disciplines, such as Graph and Molecular graph.
She most often published in these fields:
- Artificial intelligence (65.85%)
- Natural language processing (36.62%)
- Machine learning (20.77%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (65.85%)
- Machine learning (20.77%)
- Natural language processing (36.62%)
In recent papers she was focusing on the following fields of study:
Regina Barzilay mostly deals with Artificial intelligence, Machine learning, Natural language processing, Theoretical computer science and Generative grammar.
Regina Barzilay regularly ties together related areas like Context in her Artificial intelligence studies.
Regina Barzilay focuses mostly in the field of Context, narrowing it down to topics relating to Information needs and, in certain cases, Task.
Her Machine learning study incorporates themes from Variation and Representation.
Her Natural language processing research is multidisciplinary, relying on both Similarity, Word and Decipherment.
She has researched Theoretical computer science in several fields, including Correctness, Graph and Drug discovery.
Between 2018 and 2021, her most popular works were:
- A Deep Learning Approach to Antibiotic Discovery (253 citations)
- A graph-convolutional neural network model for the prediction of chemical reactivity. (159 citations)
- Analyzing Learned Molecular Representations for Property Prediction. (138 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Regina Barzilay mainly investigates Artificial intelligence, Theoretical computer science, Natural language processing, Graph and Language model.
Her Artificial intelligence research integrates issues from Machine learning and Workflow.
Her study in Theoretical computer science is interdisciplinary in nature, drawing from both Generative grammar, Transformer and Drug discovery.
Her Natural language processing research incorporates themes from Word, Task and Shot.
Her studies deal with areas such as Intervention, Randomized controlled trial, Clinical trial and Documentation as well as Task.
The Language model study combines topics in areas such as Stylometry and Fake news.
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