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Performance analysis of FFNN-based language model in contrast with n-gram
- Kim, Kwang-Ho;
- Lee, Donghyun;
- Lim, Minkyu;
- Ryang, Minho;
- Jang, Gil-Jin;
- ... Kim, J. -H.;
- 외 1명
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0초록
In this paper, we analyze the performance of feed forward neural network (FFNN)-based language model in contrast with n-gram. The probability of n-gram language model was estimated based on the statistics of word sequences. The FFNN-based language model was structured by three hidden layers, 500 hidden units per each hidden layer, and 30 dimension word embedding. The performance of FFNN-based language model is better than that of n-gram by 1.5 % in terms of WER on the English WSJ domain. © Springer International Publishing Switzerland 2015. All rights are reserved.
키워드
Feed forward neural network; Language model; N-Gram; Performance analysis
- 제목
- Performance analysis of FFNN-based language model in contrast with n-gram
- 저자
- Kim, Kwang-Ho; Lee, Donghyun; Lim, Minkyu; Ryang, Minho; Jang, Gil-Jin; Park, Jeong-Sik; Kim, J. -H.
- 발행일
- 2015-10-29
- 유형
- Book Chapter
- 저널명
- Natural Language Dialog Systems and Intelligent Assistants
- 페이지
- 253 ~ 256