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篇名
A Maximum Entropy Approach for Semantic Language Modeling
作者 Chueh, Chuang-hua (Chueh, Chuang-hua)Wang, Hsin-min (Wang, Hsin-min)Chien, Jen-tzung (Chien, Jen-tzung)
中文摘要
The conventional n-gram language model exploits only the immediate context of historical words without exploring long-distance semantic information. In this paper, we present a new information source extracted from latent semantic analysis (LSA) and adopt the maximum entropy (ME) principle to integrate it into an n-gram language model. With the ME approach, each information source serves as a set of constraints, which should be satisfied to estimate a hybrid statistical language model with maximum randomness. For comparative study, we also carry out knowledge integration via linear interpolation (LI). In the experiments on the TDT2 Chinese corpus, we find that the ME language model that combines the features of trigram and semantic information achieves a 17.9% perplexity reduction compared to the conventional trigram language model, and it outperforms the LI language model. Furthermore, in evaluation on a Mandarin speech recognition task, the ME and LI language models reduce the character error rate by 16.9% and 8.5%, respectively, over the bigram language model.
起訖頁 37-55
關鍵詞 Language modelingLatent semantic analysisMaximum entropySpeech recognition
刊名 中文計算語言學期刊  
期數 200603 (11:1期)
出版單位 中華民國計算語言學學會
該期刊-上一篇 Modeling Cantonese Pronunciation Variations for Large-Vocabulary Continuous Speech Recognition
該期刊-下一篇 Robust Target Speaker Tracking in Broadcast TV Streams
 

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