Lei Shi


Latent Topic Embedding
Di Jiang | Lei Shi | Rongzhong Lian | Hua Wu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in much finer granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent semantics by considering word co-occurrence at different levels of granularity, we propose a novel model named Latent Topic Embedding (LTE), which seamlessly integrates topic generation and embedding learning in one unified framework. We further propose an efficient Monte Carlo EM algorithm to estimate the parameters of interest. By retaining the individual advantages of topic modeling and word embedding, LTE results in better latent topics and word embedding. Extensive experiments verify the superiority of LTE over the state-of-the-arts.


Unsupervised Template Mining for Semantic Category Understanding
Lei Shi | Shuming Shi | Chin-Yew Lin | Yi-Dong Shen | Yong Rui
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


Cross Language Text Classification by Model Translation and Semi-Supervised Learning
Lei Shi | Rada Mihalcea | Mingjun Tian
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing


Improved Sentence Alignment on Parallel Web Pages Using a Stochastic Tree Alignment Model
Lei Shi | Ming Zhou
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing


A DOM Tree Alignment Model for Mining Parallel Data from the Web
Lei Shi | Cheng Niu | Ming Zhou | Jianfeng Gao
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics


An algorithm for open text semantic parsing
Lei Shi | Rada Mihalcea
Proceedings of the 3rd workshop on RObust Methods in Analysis of Natural Language Data (ROMAND 2004)

Open Text Semantic Parsing Using FrameNet and WordNet
Lei Shi | Rada Mihalcea
Demonstration Papers at HLT-NAACL 2004