SRI-LM toolkit

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ngram-count

• 計算 training data 中每個出現的 ngram 的

出現次數

3 表示 trigram

要計算 count 的 Training data

詞典檔

有 -unk: 把 OOV 當作一個符號處理

無 -unk: 遇到 OOV 就移除

輸出的 ngram count 檔名

First-Order Logic and Inference - Berlin Chen

Conditional Probability, Total Probability Theorem and ... - Berlin Chen

Linear Prediction Analysis of Speech Sounds - Berlin Chen

Solving Problems by Searching - Berlin Chen

Retrieval Models (III) - Latent Semantic Analysis (LSA) - Berlin Chen

Large Vocabulary Continuous Speech Recognition - Berlin Chen

Set-Based Model: A New Approach for Information Retrieval

Chapter 2: Discrete Random Variables: Basics - Berlin Chen

Generalized Vector Space Model In Information Retrieval

Supervised Learning - PAC, VC-Dimension etc. - Berlin Chen

Further Topics on Random Variables: Others - Berlin Chen

ngram-count • 計算 training data 中每個出現的 ngram 的出現次數 3 表示 trigram 要計算 count 的 Training data 詞典檔有 -unk: 把 OOV 當作一個符號處理無 -unk: 遇到 OOV 就移除輸出的 ngram count 檔名 8

Good-Turing Discounting and 預設的 discounting 跟 backoff 方法 Katz Backoff 前一步 ngram count 所產生的檔要輸出的 LM 檔 9

Page 1 and 2: SRI-LM toolkit 2003/12/16 Louis Tsa
Page 3 and 4: Create language model ~ W = arg max
Page 5 and 6: Vocabulary (Lexicon2003-72k.txt)
Page 7: 以 \data\ 開頭 LM Format (ssp.n
Page 11 and 12: Good-Turing Discounting Parameters
Page 13 and 14: • Take the conditional probabilit
Page 15 and 16: Witten-Bell Discounting • Key Con
Page 17 and 18: Witten-Bell Discounting p * i ci =
Page 19 and 20: • For bigram ∑ p Witten-Bell Di
Page 21 and 22: P abs Smoothing absolute discountin

SRI-LM toolkit

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