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.
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.
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.
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.
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.
Overview of the TAC 2010 Knowledge Base Population Track
Heng Ji;Heng Ji;Ralph Grishman;Hoa Trang Dang;Kira Griffitt.
Refining Event Extraction through Cross-Document Inference
Heng Ji;Ralph Grishman.
meeting of the association for computational linguistics (2008)
Joint Event Extraction via Structured Prediction with Global Features
Qi Li;Heng Ji;Liang Huang.
meeting of the association for computational linguistics (2013)
Incremental Joint Extraction of Entity Mentions and Relations
Qi Li;Heng Ji.
meeting of the association for computational linguistics (2014)
Knowledge Base Population: Successful Approaches and Challenges
Heng Ji;Ralph Grishman.
meeting of the association for computational linguistics (2011)
A Dependency-Based Neural Network for Relation Classification
Yang Liu;Furu Wei;Sujian Li;Heng Ji.
international joint conference on natural language processing (2015)
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Xiang Ren;Zeqiu Wu;Wenqi He;Meng Qu.
the web conference (2017)
A Language-Independent Neural Network for Event Detection.
Xiaocheng Feng;Lifu Huang;Duyu Tang;Heng Ji.
meeting of the association for computational linguistics (2016)
FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation
Fenglong Ma;Yaliang Li;Qi Li;Minghui Qiu.
knowledge discovery and data mining (2015)
Cross-lingual Name Tagging and Linking for 282 Languages
Xiaoman Pan;Boliang Zhang;Jonathan May;Joel Nothman.
meeting of the association for computational linguistics (2017)
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