What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Information retrieval
His primary areas of investigation include Information retrieval, World Wide Web, Relevance, Artificial intelligence and Query expansion.
As part of the same scientific family, Fabio Crestani usually focuses on Information retrieval, concentrating on Statistical model and intersecting with Key.
His work in World Wide Web covers topics such as Field which are related to areas like Sentiment analysis, Microblogging, Social media, Irony detection and Categorization.
His Relevance research incorporates themes from Document retrieval, Context, Multimedia information retrieval and Information system.
The Artificial intelligence study combines topics in areas such as Machine learning, Data mining, Measure and Natural language processing.
His work in Query expansion tackles topics such as Folksonomy which are related to areas like Vocabulary, Smoothing and Bookmarking.
His most cited work include:
- Like It or Not: A Survey of Twitter Sentiment Analysis Methods (218 citations)
- “Is this document relevant?…probably”: a survey of probabilistic models in information retrieval (197 citations)
- User's perception of relevance of spoken documents (115 citations)
What are the main themes of his work throughout his whole career to date?
Fabio Crestani mainly investigates Information retrieval, World Wide Web, Artificial intelligence, Relevance and Data mining.
His Information retrieval study frequently involves adjacent topics like Relevance feedback.
His World Wide Web study incorporates themes from Context, Multimedia and Mobile search.
Fabio Crestani interconnects Ranking, Machine learning, Pattern recognition and Natural language processing in the investigation of issues within Artificial intelligence.
The various areas that Fabio Crestani examines in his Relevance study include Task and Information needs.
Fabio Crestani has researched Query expansion in several fields, including Query language and Web search query, Web query classification.
He most often published in these fields:
- Information retrieval (50.94%)
- World Wide Web (21.72%)
- Artificial intelligence (16.09%)
What were the highlights of his more recent work (between 2015-2021)?
- Information retrieval (50.94%)
- Task (10.99%)
- World Wide Web (21.72%)
In recent papers he was focusing on the following fields of study:
Fabio Crestani spends much of his time researching Information retrieval, Task, World Wide Web, Social media and Context.
His Information retrieval research is multidisciplinary, relying on both Ranking and Point of interest.
His work deals with themes such as Natural language processing, Mobile search, Information needs and Artificial intelligence, which intersect with Task.
His World Wide Web study integrates concerns from other disciplines, such as Margin and Leverage.
The study incorporates disciplines such as Sentiment analysis, Feeling and Data science in addition to Social media.
His studies in Context integrate themes in fields like Resource management and Set.
Between 2015 and 2021, his most popular works were:
- Like It or Not: A Survey of Twitter Sentiment Analysis Methods (218 citations)
- A Test Collection for Research on Depression and Language Use (87 citations)
- Asking Clarifying Questions in Open-Domain Information-Seeking Conversations (84 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Statistics
Fabio Crestani mainly focuses on Information retrieval, Task, World Wide Web, Context and Ranking.
His Information retrieval research is multidisciplinary, incorporating elements of Point of interest and Similarity.
His Point of interest study combines topics from a wide range of disciplines, such as Popularity and Relevance.
His Task research includes themes of Social media, Personality, Fake news and Information needs.
His studies deal with areas such as Margin, Categorization and Publication as well as World Wide Web.
His Context study combines topics in areas such as Social network and Set.
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.
