What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Artificial intelligence, Natural language processing, Speech recognition, Artificial neural network and Language model.
Ruhi Sarikaya focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Pattern recognition and, in certain cases, Computation.
His biological study spans a wide range of topics, including Context, Feature and Set.
Ruhi Sarikaya has included themes like Convolutional neural network and Spoken language in his Speech recognition study.
His Artificial neural network study integrates concerns from other disciplines, such as Domain adaptation and Natural language.
His research integrates issues of Relation, Translation, Side information and Cross lingual in his study of Language model.
His most cited work include:
- Application of Deep Belief Networks for natural language understanding (300 citations)
- Convolutional neural network based triangular CRF for joint intent detection and slot filling (241 citations)
- Semantic language modeling and confidence measurement (178 citations)
What are the main themes of his work throughout his whole career to date?
Ruhi Sarikaya spends much of his time researching Artificial intelligence, Natural language processing, Speech recognition, Natural language and Domain.
His Artificial intelligence research includes elements of Machine learning and Pattern recognition.
His Natural language processing research incorporates themes from Word and Dialog box.
The study incorporates disciplines such as Feature and Vocabulary in addition to Speech recognition.
His work carried out in the field of Domain brings together such families of science as Ranking and Utterance.
His work is dedicated to discovering how Natural language understanding, Component are connected with Human–computer interaction and other disciplines.
He most often published in these fields:
- Artificial intelligence (66.14%)
- Natural language processing (46.56%)
- Speech recognition (28.57%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (66.14%)
- Natural language understanding (13.76%)
- Spoken language (12.70%)
In recent papers he was focusing on the following fields of study:
Ruhi Sarikaya focuses on Artificial intelligence, Natural language understanding, Spoken language, Domain and Natural language processing.
His Artificial intelligence research includes themes of User experience design and Machine learning.
His Natural language understanding research is multidisciplinary, relying on both Scalability, Dialog box, Speech recognition and Component.
The various areas that Ruhi Sarikaya examines in his Domain study include Recurrent neural network and Pattern recognition.
His Natural language processing research is mostly focused on the topic Natural language.
His Natural language research integrates issues from Personal life, Web service, Multimedia and Action.
Between 2016 and 2021, his most popular works were:
- The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components (103 citations)
- Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources (89 citations)
- Contextual Slot Carryover for Disparate Schemas. (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Ruhi Sarikaya mainly focuses on Artificial intelligence, Natural language processing, Scalability, Natural language understanding and Domain.
Ruhi Sarikaya carries out multidisciplinary research, doing studies in Artificial intelligence and Natural.
The Language model research Ruhi Sarikaya does as part of his general Natural language processing study is frequently linked to other disciplines of science, such as Knowledge transfer, therefore creating a link between diverse domains of science.
Ruhi Sarikaya interconnects Interpretation, Theoretical computer science and Conversation in the investigation of issues within Scalability.
His research in Natural language understanding intersects with topics in Feature, Server and Human–computer interaction.
His studies in Domain integrate themes in fields like Context model, Representation, Natural language and Action.
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