D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 4,238 185 World Ranking 8565 National Ranking 266

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

Artificial intelligence, Natural language processing, Machine learning, Support vector machine and Semantics are his primary areas of study. His Artificial intelligence study frequently links to adjacent areas such as Pattern recognition. His study in Natural language processing is interdisciplinary in nature, drawing from both Speech recognition, Task, FrameNet and Linguistics.

The various areas that he examines in his Semantics study include Tree structure and Robot. His Tree kernel study incorporates themes from Semantic role labeling and Graph kernel. Roberto Basili interconnects Feature engineering, Parse tree and Perceptron in the investigation of issues within Feature.

His most cited work include:

  • Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification (197 citations)
  • Complex Linguistic Features for Text Classification: A Comprehensive Study (155 citations)
  • Tree kernels for semantic role labeling (154 citations)

What are the main themes of his work throughout his whole career to date?

Roberto Basili spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Support vector machine and Information extraction. Within one scientific family, Roberto Basili focuses on topics pertaining to Domain under Artificial intelligence, and may sometimes address concerns connected to Ontology. Roberto Basili has included themes like Task and FrameNet in his Natural language processing study.

His work investigates the relationship between Support vector machine and topics such as Sentiment analysis that intersect with problems in Lexicon. His Tree kernel research incorporates themes from Feature engineering, Parse tree, Inference and Kernel. His Semantic role labeling study typically links adjacent topics like Semantics.

He most often published in these fields:

  • Artificial intelligence (63.70%)
  • Natural language processing (47.52%)
  • Machine learning (16.17%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (63.70%)
  • Deep learning (4.29%)
  • Seismology (9.57%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Deep learning, Seismology, Kernel and Machine learning. His Artificial intelligence study incorporates themes from Relevance and Natural language processing. His research integrates issues of Frame semantics and FrameNet in his study of Natural language processing.

His Deep learning study also includes fields such as

  • Question answering that intertwine with fields like Telecommunications, Scale and Field,
  • Image, which have a strong connection to Recurrent neural network, Convolutional neural network and Speech recognition. His Kernel research is multidisciplinary, relying on both Artificial neural network and Support vector machine, Pattern recognition. His Machine learning research is multidisciplinary, incorporating elements of Adversarial system, Generative grammar, Virtual learning environment and Java collections framework.

Between 2017 and 2021, his most popular works were:

  • KELP: a Kernel-based Learning Platform (34 citations)
  • GAN-BERT: Generative adversarial learning for robust text classification with a bunch of labeled examples (11 citations)
  • Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures. (8 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Linguistics

The scientist’s investigation covers issues in Artificial intelligence, Kernel, Machine learning, Deep learning and Pattern recognition. His Artificial intelligence research incorporates elements of Relevance and Natural language processing. His work deals with themes such as Field and Neural learning, which intersect with Natural language processing.

His research in Machine learning intersects with topics in Adversarial system, Generative grammar, Virtual learning environment and Java collections framework. In his research, Data science and Scale is intimately related to Question answering, which falls under the overarching field of Deep learning. The study incorporates disciplines such as Irony, Sarcasm and Speech act in addition to Pattern recognition.

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.

Best Publications

Complex Linguistic Features for Text Classification: A Comprehensive Study

Alessandro Moschitti;Roberto Basili.
european conference on information retrieval (2004)

264 Citations

Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification

Alessandro Moschitti;Silvia Quarteroni;Roberto Basili;Suresh Manandhar.
meeting of the association for computational linguistics (2007)

242 Citations

Tree kernels for semantic role labeling

Alessandro Moschitti;Alessandro Moschitti;Alessandro Moschitti;Daniele Pighin;Daniele Pighin;Daniele Pighin;Roberto Basili;Roberto Basili;Roberto Basili.
Computational Linguistics (2008)

195 Citations

Building the Italian Syntactic-Semantic Treebank

Simonetta Montemagni;Francesco Barsotti;Marco Battista;Nicoletta Calzolari.
TEXT, SPEECH AND LANGUAGE TECHNOLOGY (2003)

159 Citations

Structured Lexical Similarity via Convolution Kernels on Dependency Trees

Danilo Croce;Alessandro Moschitti;Roberto Basili.
empirical methods in natural language processing (2011)

156 Citations

Identification of relevant terms to support the construction of domain ontologies

Paola Velardi;Michele Missikoff;Roberto Basili.
human language technology (2001)

140 Citations

Classification of musical genre: a machine learning approach.

Roberto Basili;Alfredo Serafini;Armando Stellato.
international symposium/conference on music information retrieval (2004)

106 Citations

A context-based model for Sentiment Analysis in Twitter

Andrea Vanzo;Danilo Croce;Roberto Basili.
international conference on computational linguistics (2014)

101 Citations

Parsing engineering and empirical robustness

Roberto Basili;Fabio Massimo Zanzotto.
Natural Language Engineering (2002)

99 Citations

KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers

Simone Filice;Danilo Croce;Alessandro Moschitti;Roberto Basili.
north american chapter of the association for computational linguistics (2016)

96 Citations

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