Wanxiang Che mainly investigates Artificial intelligence, Natural language processing, Parsing, Word and Dependency grammar. His research integrates issues of Named-entity recognition and Pattern recognition in his study of Artificial intelligence. His study in the field of Sentence is also linked to topics like Encoder.
His study connects Discriminative model and Parsing. His study in Word is interdisciplinary in nature, drawing from both Relation and Semantic similarity. His Dependency grammar research integrates issues from S-attributed grammar, Graph and Syntactic predicate.
His main research concerns Artificial intelligence, Natural language processing, Parsing, Word and Dependency grammar. His Artificial intelligence research incorporates elements of Machine learning, Named-entity recognition and Pattern recognition. His Natural language processing research includes themes of Margin, Context and Speech recognition.
His research in Parsing intersects with topics in Annotation and Graph. Wanxiang Che interconnects Pipeline, Semantic role labeling and Focus in the investigation of issues within Word. Wanxiang Che has researched Dependency grammar in several fields, including Language model, Space and Syntactic predicate.
His primary areas of study are Artificial intelligence, Natural language processing, Spoken language, Graph and Transformer. His Artificial intelligence study which covers Machine learning that intersects with Language understanding. Wanxiang Che works in the field of Natural language processing, namely Sentence.
His research investigates the connection with Graph and areas like Utterance which intersect with concerns in Graph traversal. His Transformer research is multidisciplinary, relying on both F1 score, Computer engineering and Task. In his study, Parsing is inextricably linked to Pattern recognition, which falls within the broad field of Projection.
Wanxiang Che focuses on Artificial intelligence, Natural language processing, Conversation, Human–computer interaction and Graph. His Artificial intelligence study frequently intersects with other fields, such as Machine learning. He mostly deals with Parsing in his studies of Natural language processing.
His Conversation study integrates concerns from other disciplines, such as Consistency, Coherence and Knowledge graph. His biological study spans a wide range of topics, including Context and Reinforcement learning. His Graph research includes elements of Machine reading, Comprehension and Utterance.
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LTP: A Chinese Language Technology Platform
Wanxiang Che;Zhenghua Li;Ting Liu.
international conference on computational linguistics (2010)
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui;Wanxiang Che;Ting Liu;Bing Qin.
IEEE Transactions on Audio, Speech, and Language Processing (2021)
Learning Semantic Hierarchies via Word Embeddings
Ruiji Fu;Jiang Guo;Bing Qin;Wanxiang Che.
meeting of the association for computational linguistics (2014)
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency.
Shuhuai Ren;Yihe Deng;Kun He;Wanxiang Che.
meeting of the association for computational linguistics (2019)
Cross-lingual Dependency Parsing Based on Distributed Representations
Jiang Guo;Wanxiang Che;David Yarowsky;Haifeng Wang.
international joint conference on natural language processing (2015)
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation
Wanxiang Che;Yijia Liu;Yuxuan Wang;Bo Zheng.
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2018)
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Yiming Cui;Wanxiang Che;Ting Liu;Bing Qin.
empirical methods in natural language processing (2020)
Revisiting Embedding Features for Simple Semi-supervised Learning
Jiang Guo;Wanxiang Che;Haifeng Wang;Ting Liu.
empirical methods in natural language processing (2014)
Convolution Neural Network for Relation Extraction
Chunyang Liu;Wenbo Sun;Wenhan Chao;Wanxiang Che.
advanced data mining and applications (2013)
A stack-propagation framework with token-level intent detection for spoken language understanding
Libo Qin;Wanxiang Che;Yangming Li;Haoyang Wen.
empirical methods in natural language processing (2019)
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