What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- The Internet
- World Wide Web
Gabriella Kazai mainly investigates Information retrieval, Crowdsourcing, Data science, Relevance and Data mining.
Her biological study spans a wide range of topics, including XML, Efficient XML Interchange, Document Structure Description and XML retrieval.
The various areas that Gabriella Kazai examines in her Crowdsourcing study include Majority rule, Robustness, Book search and Statistical model.
Her Data science research incorporates themes from Quality and IR evaluation.
Her Quality study combines topics from a wide range of disciplines, such as Crowds, World Wide Web, Search engine and Set.
Her research integrates issues of Confusion matrix and Bayesian inference in her study of Data mining.
Her most cited work include:
- Initiative for the Evaluation of XML Retrieval (330 citations)
- Community-based bayesian aggregation models for crowdsourcing (150 citations)
- In search of quality in crowdsourcing for search engine evaluation (126 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Information retrieval, World Wide Web, Relevance, XML and Crowdsourcing.
Her research is interdisciplinary, bridging the disciplines of XML retrieval and Information retrieval.
In her research, Focus is intimately related to Multimedia, which falls under the overarching field of World Wide Web.
In her study, which falls under the umbrella issue of Relevance, Document Structure Description is strongly linked to Document retrieval.
Her work blends XML and Set studies together.
She combines subjects such as Crowds and Data science with her study of Crowdsourcing.
She most often published in these fields:
- Information retrieval (66.67%)
- World Wide Web (32.62%)
- Relevance (21.99%)
What were the highlights of her more recent work (between 2012-2021)?
- Information retrieval (66.67%)
- World Wide Web (32.62%)
- Relevance (21.99%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Information retrieval, World Wide Web, Relevance, Crowdsourcing and Multimedia.
Her study looks at the intersection of Information retrieval and topics like Computational linguistics with Metadata.
Her study in the fields of Social media, Recommender system, Mobile apps and XML under the domain of World Wide Web overlaps with other disciplines such as Engineering.
Her studies deal with areas such as Web page, Pooling and Set as well as Relevance.
Her Set course of study focuses on Quality and Crowds.
The study incorporates disciplines such as Knowledge management, Machine learning, Data science and Artificial intelligence in addition to Crowdsourcing.
Between 2012 and 2021, her most popular works were:
- Community-based bayesian aggregation models for crowdsourcing (150 citations)
- An analysis of human factors and label accuracy in crowdsourcing relevance judgments (80 citations)
- Quality Management in Crowdsourcing using Gold Judges Behavior (29 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- World Wide Web
Gabriella Kazai focuses on Information retrieval, World Wide Web, Relevance, Crowdsourcing and Test.
Gabriella Kazai has included themes like Computational linguistics and Electronic publishing in her Information retrieval study.
She has researched World Wide Web in several fields, including Multimedia and Profiling.
Her Relevance research includes themes of Quality and Social media, User-generated content.
Her work carried out in the field of Quality brings together such families of science as Crowds, Data science and Set.
Her Crowdsourcing study also includes fields such as
- Artificial intelligence, which have a strong connection to Quality management,
- Machine learning which intersects with area such as Data mining, Bayesian inference and Bayesian probability.
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.
