What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Diego R. Amancio mainly investigates Artificial intelligence, Natural language processing, Word, Text mining and Semantics.
Many of his studies on Artificial intelligence apply to Machine learning as well.
Specifically, his work in Natural language processing is concerned with the study of Machine translation.
His study in Word is interdisciplinary in nature, drawing from both Decision tree, Syntax, Adjacency list and Topology.
His research in Text mining intersects with topics in Entropy and Data science.
His Semantics study integrates concerns from other disciplines, such as Reading, Dependency, Portuguese, Syntax and Natural language.
His most cited work include:
- A systematic comparison of supervised classifiers. (119 citations)
- Clustering algorithms: a comparative approach (112 citations)
- Using network science and text analytics to produce surveys in a scientific topic (69 citations)
What are the main themes of his work throughout his whole career to date?
Diego R. Amancio mostly deals with Artificial intelligence, Natural language processing, Context, Word and Identification.
While the research belongs to areas of Artificial intelligence, he spends his time largely on the problem of Structure, intersecting his research to questions surrounding Network model.
His work carried out in the field of Natural language processing brings together such families of science as Representation, Relevance, Adjacency list and Representation.
He interconnects Data mining and Cluster analysis in the investigation of issues within Context.
His Word research incorporates elements of Portuguese and Semantic network.
His Identification research integrates issues from Writing style and Similarity.
He most often published in these fields:
- Artificial intelligence (62.79%)
- Natural language processing (46.51%)
- Context (23.26%)
What were the highlights of his more recent work (between 2019-2021)?
- Natural language processing (46.51%)
- Artificial intelligence (62.79%)
- Pairwise comparison (3.10%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Natural language processing, Artificial intelligence, Pairwise comparison, Visibility and Network science.
His Natural language processing study which covers Representation that intersects with Thresholding.
His Artificial intelligence research includes elements of Feature relevance and Identification.
The concepts of his Feature relevance study are interwoven with issues in Textual information and Data science.
His work deals with themes such as Complex system, Similarity and Pattern recognition, which intersect with Identification.
His Semantic network research is multidisciplinary, relying on both Flow, Semantic similarity, Markov chain, Semantic field and Representation.
Between 2019 and 2021, his most popular works were:
- Complex systems: Features, similarity and connectivity (11 citations)
- Semantic flow in language networks discriminates texts by genre and publication date (5 citations)
- A complex network approach to political analysis: Application to the Brazilian Chamber of Deputies. (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary scientific interests are in Co-occurrence networks, Natural language processing, Semantic network, Artificial intelligence and Preferential attachment.
Co-occurrence networks is integrated with Semantic field, Flow, Semantic similarity, Markov chain and Representation in his research.
His research integrates issues of Representation and Word, Word2vec in his study of Natural language processing.
Diego R. Amancio performs multidisciplinary studies into Semantic network and Network science in his work.
Diego R. Amancio combines Preferential attachment and Cognitive psychology in his research.
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.
