Qi Han


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Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification
Maximilian Köper | Melanie Zaiß | Qi Han | Steffen Koch | Sabine Schulte im Walde
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Vector space models and distributional information are widely used in NLP. The models typically rely on complex, high-dimensional objects. We present an interactive visualisation tool to explore salient lexical-semantic features of high-dimensional word objects and word similarities. Most visualisation tools provide only one low-dimensional map of the underlying data, so they are not capable of retaining the local and the global structure. We overcome this limitation by providing an additional trust-view to obtain a more realistic picture of the actual object distances. Additional tool options include the reference to a gold standard classification, the reference to a cluster analysis as well as listing the most salient (common) features for a selected subset of the words.


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A tunable language model for statistical machine translation
Junfei Guo | Juan Liu | Qi Han | Andreas Maletti
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

A novel variation of modified KNESER-NEY model using monomial discounting is presented and integrated into the MOSES statistical machine translation toolkit. The language model is trained on a large training set as usual, but its new discount parameters are tuned to the small development set. An in-domain and cross-domain evaluation of the language model is performed based on perplexity, in which sizable improvements are obtained. Additionally, the performance of the language model is also evaluated in several major machine translation tasks including Chinese-to-English. In those tests, the test data is from a (slightly) different domain than the training data. The experimental results indicate that the new model significantly outperforms a baseline model using SRILM in those domain adaptation scenarios. The new language model is thus ideally suited for domain adaptation without sacrificing performance on in-domain experiments.


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CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter Text
Qi Han | Junfei Guo | Hinrich Schuetze
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)