Dongxu Zhang

Selected Publications

[3] Dongxu Zhang, Boliang Zhang, Xiaoman Pan, Xiaocheng Feng, Heng Ji and Weiran Xu. "Bitext Name Tagging for Cross-lingual Entity Annotation Projection" . In COLING 2016 [pdf]

[2] Dongxu Zhang, and Dong Wang.“Relation Classification via Recurrent Neural Network”. arXiv:1508.01006. [pdf]

[1] Dongxu Zhang, Bin Yuan, Dong Wang, Rong Liu. “Joint Semantic Relevance Learning with Text Data and Graph Knowledge”, In the third Workshop of CVSC, ACL 2015. [pdf]

(Find more papers at Google scholar)

Experience

• Amazon.com, Inc. (6/2018-8/2018)

Mentor: Subhabrata Mukherjee and Xin Luna Dong.

• Institute of Computational Linguistics, Peking University (5/2017-7/2017)

Advisor: Sujian Li

• Naturali, Beijing, China (2/2017-4/2017)

Mentor: Dekang Lin

• Blender Lab, Rensselaer Polytechnic Institute (4/2016-6/2016)

Advisor: Heng Ji

• CSLT, Tsinghua University (10/2014-3/2016)

Advisor: Dong Wang

• PRIS Lab, Beijing University of Posts and Telecommunications (9/2013-09/2014)

Advisor: Weiran Xu

• Samsung Telecommunication R&D Center, Beijing, China (5/2013-9/2013)

Mentor: Xiaojie Yu

(Add me at Linkedin)

Code & Data

KBP37

KBP37 is a dataset for sentence level relation classification task. Compared with SemEval-2010 Task 8, this dataset has longer relation mentions in average length. See my paper for more details. The dataset is a revision of MIML-RE dataset. Please cite this paper and MIML-RE for the use of research purpose. In this dataset, an acceptable system should reach at least 50% in F-1 value. (Much harder than SemEval-2010 Task 8)

Animal-143

This dataset contains semantic relatedness scores between 143 pairs of animal names. The score of each pair was evaluated by 9 people in CSLT, and the average is used. This dataset can be regarded as a domain-specific evaluation dataset. (And the well-known WordSimilarity-353 dataset can be regarded as a general-domain dataset.) In this dataset, an acceptable system should reach at least 0.7 in spearman coefficient.

Sentence classification

Here you can find CNN+Pooling code for relation classification, sentiment classification and answer selection implemented with Tensorflow.

(Download more resources from Github)

Misc.