Jun Lang


Dependency Parsing with Partial Annotations: An Empirical Comparison
Yue Zhang | Zhenghua Li | Jun Lang | Qingrong Xia | Min Zhang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA). The first approach is based on a forest-based training objective for two CRF parsers, i.e., a biaffine neural network graph-based parser (Biaffine) and a traditional log-linear graph-based parser (LLGPar). The second approach is based on the idea of constrained decoding for three parsers, i.e., a traditional linear graph-based parser (LGPar), a globally normalized neural network transition-based parser (GN3Par) and a traditional linear transition-based parser (LTPar). For the test phase, constrained decoding is also used for completing partial trees. We conduct experiments on Penn Treebank under three different settings for simulating PA, i.e., random, most uncertain, and divergent outputs from the five parsers. The results show that LLGPar is most effective in directly learning from PA, and other parsers can achieve best performance when PAs are completed into full trees by LLGPar.


An Iterative Link-based Method for Parallel Web Page Mining
Le Liu | Yu Hong | Jun Lu | Jun Lang | Heng Ji | Jianmin Yao
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


I2R’s machine translation system for IWSLT 2010
Xiangyu Duan | Rafael Banchs | Jun Lang | Deyi Xiong | Aiti Aw | Min Zhang | Haizhou Li
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign


An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming
Xiaofeng Yang | Jian Su | Jun Lang | Chew Lim Tan | Ting Liu | Sheng Li
Proceedings of ACL-08: HLT