What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Linguistics
His main research concerns Artificial intelligence, Natural language processing, Word, Semantic similarity and Semantics.
Peter D. Turney frequently studies issues relating to Machine learning and Artificial intelligence.
He has included themes like Analogy and Meaning in his Natural language processing study.
His study in Word is interdisciplinary in nature, drawing from both Vector space model, Relation, Phrase and Lexicon.
His Semantic similarity research also works with subjects such as
- Pointwise mutual information and related Semantic role labeling, Noun and Latent semantic analysis,
- Orientation, which have a strong connection to Mutual information.
His work deals with themes such as Similarity, Information extraction, Random indexing, Distributional semantics and Structure, which intersect with Semantics.
His most cited work include:
- From frequency to meaning: vector space models of semantics (2084 citations)
- Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews (1620 citations)
- Measuring praise and criticism: Inference of semantic orientation from association (1319 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Word, Machine learning and Semantics.
Peter D. Turney integrates Artificial intelligence and Synonym in his studies.
His research in the fields of Semantic similarity and Noun overlaps with other disciplines such as Set.
The study incorporates disciplines such as Ontology, Ranking, Phrase and Lexicon in addition to Word.
Peter D. Turney works mostly in the field of Machine learning, limiting it down to topics relating to Training set and, in certain cases, Context and Statistical classification.
His Semantics research includes themes of Structure, Similarity, Meaning and Natural language.
He most often published in these fields:
- Artificial intelligence (70.18%)
- Natural language processing (42.98%)
- Word (21.05%)
What were the highlights of his more recent work (between 2012-2019)?
- Artificial intelligence (70.18%)
- Natural language processing (42.98%)
- Word (21.05%)
In recent papers he was focusing on the following fields of study:
Peter D. Turney mainly focuses on Artificial intelligence, Natural language processing, Word, Semantics and Information retrieval.
His Artificial intelligence research is multidisciplinary, relying on both Statement, Meaning and Literal.
His research investigates the connection between Natural language processing and topics such as Annotation that intersect with issues in Parsing, Identification, Information extraction and Multiple choice.
As a part of the same scientific family, Peter D. Turney mostly works in the field of Word, focusing on Association and, on occasion, Sentiment analysis and Affect.
His Semantics research is multidisciplinary, incorporating elements of Context, Similarity, Synonym and WordNet.
His research on Information retrieval also deals with topics like
- Bibliography which intersects with area such as Feature selection,
- Key and related Data science, Unsupervised learning, Terminology, Leverage and Simple.
Between 2012 and 2019, his most popular works were:
- CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON (987 citations)
- Measuring academic influence: Not all citations are equal (124 citations)
- Combining retrieval, statistics, and inference to answer elementary science questions (112 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Linguistics
Artificial intelligence, Natural language processing, Term, Crowds and Crowdsourcing are his primary areas of study.
His Artificial intelligence research integrates issues from Contrast, Speech recognition, Meaning, Word and Existential quantification.
Peter D. Turney specializes in Natural language processing, namely Machine translation.
A majority of his Term research is a blend of other scientific areas, such as Lexicon, Association, Word, Annotation and Sentiment analysis.
His research combines Affect and Crowds.
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