What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary areas of study are Artificial intelligence, Natural language processing, Machine learning, Entity linking and Knowledge base.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Event, Contrast and Data mining.
His Natural language processing research is multidisciplinary, incorporating elements of Space, Word and Database.
His biological study spans a wide range of topics, including Scheme, Layer and Pipeline.
When carried out as part of a general Entity linking research project, his work on Weak entity is frequently linked to work in Population, therefore connecting diverse disciplines of study.
Heng Ji focuses mostly in the field of Surprise, narrowing it down to topics relating to Annotation and, in certain cases, Visualization.
His most cited work include:
- Overview of the TAC 2010 Knowledge Base Population Track (324 citations)
- Overview of the TAC 2010 Knowledge Base Population Track (324 citations)
- Joint Event Extraction via Structured Prediction with Global Features (302 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, Information retrieval, Information extraction and Knowledge base.
His Artificial intelligence research is multidisciplinary, relying on both Event and Machine learning.
His study in Natural language processing is interdisciplinary in nature, drawing from both Domain, Speech recognition and Coreference.
His work in the fields of Information retrieval, such as Ranking, overlaps with other areas such as Cross media.
His work carried out in the field of Information extraction brings together such families of science as Pipeline, Data mining, Inference and Data science.
His research integrates issues of Context and Knowledge extraction in his study of Knowledge base.
He most often published in these fields:
- Artificial intelligence (58.90%)
- Natural language processing (45.89%)
- Information retrieval (22.95%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (58.90%)
- Natural language processing (45.89%)
- Benchmark (6.51%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Natural language processing, Benchmark, Event and Annotation.
Heng Ji regularly links together related areas like Machine learning in his Artificial intelligence studies.
The Natural language processing study combines topics in areas such as Domain and Word, Word embedding.
His Benchmark research includes themes of Speech translation, Machine translation, Space, Embedding and Consistency.
The concepts of his Event study are interwoven with issues in Argument and Coreference.
Heng Ji works mostly in the field of Relationship extraction, limiting it down to concerns involving Graph and, occasionally, Information retrieval.
Between 2018 and 2021, his most popular works were:
- The Age of Social Sensing (80 citations)
- Joint Entity and Event Extraction with Generative Adversarial Imitation Learning (35 citations)
- Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Heng Ji mostly deals with Artificial intelligence, Natural language processing, Graph, Event and Information retrieval.
His study involves Information extraction, Recurrent neural network, Edit distance, Encoding and Cluster analysis, a branch of Artificial intelligence.
His Information extraction study integrates concerns from other disciplines, such as Generative adversarial network, Sentence, Imitation, Pairwise comparison and Machine learning.
His work on Relationship extraction as part of general Natural language processing research is frequently linked to Heuristics, bridging the gap between disciplines.
His Event research includes elements of Adversarial system, Annotation, Space, Argument and Imitation learning.
Heng Ji interconnects Simple and Turing in the investigation of issues within Information retrieval.
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