His primary scientific interests are in Artificial intelligence, Natural language processing, Information retrieval, Web page and Machine learning. His research in Artificial intelligence intersects with topics in Isolation, Probability distribution and Cache. Shuming Shi has researched Natural language processing in several fields, including Similarity and Inference.
His research investigates the connection with Information retrieval and areas like Ranking which intersect with concerns in Vector space model, Human–computer information retrieval, Cognitive models of information retrieval and Image retrieval. His studies deal with areas such as Class, Corpus based, F1 score and Font as well as Web page. His work on Artificial neural network as part of general Machine learning research is often related to Word problem, thus linking different fields of science.
Shuming Shi mainly investigates Artificial intelligence, Natural language processing, Machine translation, Information retrieval and Translation. Shuming Shi focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Matching. His work on Syntax, Language model and Phrase as part of his general Natural language processing study is frequently connected to Structure, thereby bridging the divide between different branches of science.
His research in the fields of BLEU overlaps with other disciplines such as Transformer. Information retrieval is closely attributed to Web page in his research. His research integrates issues of NIST and Representation in his study of Translation.
Shuming Shi mostly deals with Artificial intelligence, Natural language processing, Machine translation, Translation and Machine learning. Shuming Shi undertakes multidisciplinary investigations into Artificial intelligence and Conversation in his work. His Natural language processing research includes themes of Artificial neural network and Social media.
His Machine translation study combines topics from a wide range of disciplines, such as Theoretical computer science and Inference. His Translation study combines topics in areas such as NIST, Representation and Reinforcement learning. The various areas that Shuming Shi examines in his Machine learning study include Matching, Training set and Empirical research.
His scientific interests lie mostly in Artificial intelligence, Machine translation, Natural language processing, Translation and Machine learning. His work blends Artificial intelligence and Conversation studies together. His Machine translation research focuses on subjects like Theoretical computer science, which are linked to Pooling, Rule-based machine translation and Key.
His Sentence and Phrase study, which is part of a larger body of work in Natural language processing, is frequently linked to Space, bridging the gap between disciplines. The Translation study combines topics in areas such as NIST, Syntax and Word. His Relevance study in the realm of Machine learning connects with subjects such as Quality.
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.
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
Yan Song;Shuming Shi;Jing Li;Haisong Zhang.
north american chapter of the association for computational linguistics (2018)
Web object retrieval
Zaiqing Nie;Yunxiao Ma;Shuming Shi;Ji-Rong Wen.
the web conference (2007)
SOMO: Self-Organized Metadata Overlay for Resource Management in P2P DHT
Zheng Zhang;Shu-Ming Shi;Jing Zhu.
international workshop on peer-to-peer systems (2003)
Learning to Remember Translation History with a Continuous Cache
Zhaopeng Tu;Yang Liu;Shuming Shi;Tong Zhang.
Transactions of the Association for Computational Linguistics (2018)
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning
Shuming Shi;Yuehui Wang;Chin-Yew Lin;Xiaojiang Liu.
empirical methods in natural language processing (2015)
Deep Neural Solver for Math Word Problems
Yan Wang;Xiaojiang Liu;Shuming Shi.
empirical methods in natural language processing (2017)
Making peer-to-peer keyword searching feasible using multi-level partitioning
Shuming Shi;Guangwen Yang;Dingxing Wang;Jin Yu.
international workshop on peer-to-peer systems (2004)
Overview of NTCIR-9 RITE : Recognizing Inference in TExt
Hideki Shima;Hiroshi Kanayama;Cheng-Wei Lee;Chuan-Jie Lin.
Proceedings of the 9th NTCIR Workshop, 2011 (2011)
Title extraction from bodies of HTML documents and its application to web page retrieval
Yunhua Hu;Guomao Xin;Ruihua Song;Guoping Hu.
international acm sigir conference on research and development in information retrieval (2005)
Adding dominant media elements to search results
Ming Jing Li;Shuming Shi;Wei-Ying Ma;Zhiwei Li.
Profile was last updated on December 6th, 2021.
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