What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Natural language processing
- Machine learning
Artificial intelligence, Natural language processing, Information retrieval, Question answering and WordNet are her primary areas of study.
Sanda M. Harabagiu works mostly in the field of Artificial intelligence, limiting it down to topics relating to Relation and, in certain cases, Textual entailment and Filter.
Her study in Natural language processing is interdisciplinary in nature, drawing from both Negation and Contradiction.
Her work on Automatic summarization as part of her general Information retrieval study is frequently connected to Net, thereby bridging the divide between different branches of science.
Her Question answering research incorporates themes from Context and Semantics.
In the field of WordNet, her study on eXtended WordNet overlaps with subjects such as Resource and Falcon.
Her most cited work include:
- Using Predicate-Argument Structures for Information Extraction (339 citations)
- FALCON: Boosting Knowledge for Answer Engines (252 citations)
- Performance issues and error analysis in an open-domain question answering system (210 citations)
What are the main themes of her work throughout her whole career to date?
Sanda M. Harabagiu mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Question answering and WordNet.
Her study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning.
Her research links Semantics with Natural language processing.
In general Information retrieval, her work in Automatic summarization, Ranking, Unified Medical Language System and Relevance is often linked to Medical record linking many areas of study.
Her research investigates the connection between Question answering and topics such as Inference that intersect with issues in Knowledge-based systems.
The concepts of her Information extraction study are interwoven with issues in Relation, Knowledge base and Knowledge acquisition.
She most often published in these fields:
- Artificial intelligence (66.87%)
- Natural language processing (57.67%)
- Information retrieval (35.58%)
What were the highlights of her more recent work (between 2014-2020)?
- Artificial intelligence (66.87%)
- Natural language processing (57.67%)
- Deep learning (6.75%)
In recent papers she was focusing on the following fields of study:
Sanda M. Harabagiu spends much of her time researching Artificial intelligence, Natural language processing, Deep learning, Information retrieval and Electroencephalography.
Sanda M. Harabagiu combines subjects such as Machine learning, Computer vision and Identification with her study of Artificial intelligence.
Her Natural language processing research incorporates elements of Dependency, Semantics and Knowledge graph.
Her Deep learning research is multidisciplinary, relying on both Clinical trial and Negation.
She interconnects Probabilistic logic, Data science and Knowledge representation and reasoning in the investigation of issues within Information retrieval.
Her Probabilistic logic study incorporates themes from Question answering, Clinical decision support system, Inference and Medical algorithm.
Between 2014 and 2020, her most popular works were:
- Medical Question Answering for Clinical Decision Support (33 citations)
- Multi-modal Patient Cohort Identification from EEG Report and Signal Data. (12 citations)
- The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports. (11 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Sanda M. Harabagiu focuses on Artificial intelligence, Deep learning, Electroencephalography, Natural language processing and Medical record.
Many of her studies on Artificial intelligence apply to Computer vision as well.
Her work deals with themes such as Ontology, Relevance and Identification, which intersect with Deep learning.
She focuses mostly in the field of Natural language processing, narrowing it down to topics relating to Variety and, in certain cases, Data mining, Annotation and Modality.
Her study looks at the relationship between Probabilistic logic and topics such as Information retrieval, which overlap with Knowledge representation and reasoning.
Her work on Question answering is being expanded to include thematically relevant topics such as Data science.
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