Language Model Methods and MetricsSkip N-gramsSkip N-gram Learning CurveContent Word Language ModelContent Word ModelBag Generation MetricsSlide 7Clustering -IBMFullPredictLearning Curve for IBMFullPredictLanguage Model Methods and MetricsGary LuuRyan FortuneSkip N-grams•Interpolated with Bigram•Get Influence of words further away without increasing dimensionality•Learning CurveSkip N-gram Learning CurveContent Word Language Model•Help predict next word using last uncommon word, try to capture context•Found list of 250 most common words•Tried different sizes for common words•Interpolated with language models, since this wouldn’t maintain grammar•P(w|C)Content Word ModelBag Generation Metrics•Bag Generation – NP-Hard•Random Restart Greedy Hill-Climbing•Stability Metric•Give model correct sentence, does it maintain it as an optima? •A percentage of sentences that remain stable•Reconstruction Metric•Needs to be compared against lucky/randomBag Generation MetricsClustering -IBMFullPredict•Clustering overview•Perplexity down to 107 with million sentence corpus•Pibmfullpredict(wi|wi-2wi-1) = [λP(W|wi-2wi-1) + (1-λ)P(W|Wi-1Wi-2)] * [μP(w|wi-1wi-2,W) + (1-μ)P(w|Wi-2,Wi-1,W)]Learning Curve for
View Full Document
Unlocking...