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Lina Zhou, Member, IEEE, Yongmei Shi, and Dongsong Zhang 報告者:黃烱育 2015/11/231 碩研資工一甲 M97G0217 黃烱育.

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Presentation on theme: "Lina Zhou, Member, IEEE, Yongmei Shi, and Dongsong Zhang 報告者:黃烱育 2015/11/231 碩研資工一甲 M97G0217 黃烱育."— Presentation transcript:

1 Lina Zhou, Member, IEEE, Yongmei Shi, and Dongsong Zhang 報告者:黃烱育 2015/11/231 碩研資工一甲 M97G0217 黃烱育

2 Outline Introduction Statistical Language Models Data Sets Discussion Conclusion 2015/11/232 碩研資工一甲 M97G0217 黃烱育

3 Introduction There is a growing need develop effective ways to detect online deception. That developing SLMs does not require an explicit feature selection process. 2015/11/233 碩研資工一甲 M97G0217 黃烱育

4 Statistical Language Models(1) n-gram models Predicting the next word by the probability function 2015/11/234 碩研資工一甲 M97G0217 黃烱育 Next Word History

5 Statistical Language Models(2) n-gram models Example of words sequence 2015/11/235 碩研資工一甲 M97G0217 黃烱育 Sue swallowed the green __. Pill Tree 0st->unigrams model 1st ->bigrams model 2st->trigrams model 3st->four-grams model

6 Statistical Language Models(3) Statistical Estimators Prediction probability estimation 2015/11/236 碩研資工一甲 M97G0217 黃烱育 數值會小於 1 限制條件較多 限制條件較少

7 Statistical Language Models(4) Maximum Likelihood Estimation N-gram model by MLE 2015/11/237 碩研資工一甲 M97G0217 黃烱育

8 Data Sets(1) 2015/11/23 8 碩研資工一甲 M97G0217 黃烱育 欺騙真實

9 Data Sets(2) 2015/11/23 9 碩研資工一甲 M97G0217 黃烱育 …… Monologues Dyads Triads

10 Discussion-Unigrams(1) 2015/11/2310 碩研資工一甲 M97G0217 黃烱育

11 Discussion-Bigrams(2) 2015/11/2311 碩研資工一甲 M97G0217 黃烱育

12 Conclusion This is the first study that used SLMs to detect online deception. The diversity of the data sets used for systematic evaluation was also unprecedented. This study empirically demonstrated that SLMs not only overcame the limitations of traditional feature- based classification models, but also achieved more competitive performance in comparison to SVM models. 2015/11/2312 碩研資工一甲 M97G0217 黃烱育

13 2015/11/23 碩研資工一甲 M97G0217 黃烱育 13

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19 Statistical Language Models Advantages Not waste any probability mass on events that are not in the training corpus Give the highest probability to the training corpus Problems Unsuitable for statistical inference in NLP Sparseness->zero probability of a long string Solutions Discounting methods : referred as smoothing 2015/11/2319 碩研資工一甲 M97G0217 黃烱育

20 Statistical Language Models Kneser-Ney smoothing method The smoothing method is described : 2015/11/2320 碩研資工一甲 M97G0217 黃烱育


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