1 / 9

Language Model Methods and Metrics

Language Model Methods and Metrics. Gary Luu Ryan Fortune. Skip N-grams. Interpolated with Bigram Get Influence of words further away without increasing dimensionality Learning Curve. Skip N-gram Learning Curve. Content Word Language Model.

dara-burke
Download Presentation

Language Model Methods and Metrics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Language Model Methods and Metrics Gary Luu Ryan Fortune

  2. Skip N-grams • Interpolated with Bigram • Get Influence of words further away without increasing dimensionality • Learning Curve

  3. Skip N-gram Learning Curve

  4. Content 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)

  5. Content Word Model

  6. Bag 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/random

  7. Bag Generation Metrics

  8. Clustering -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)]

  9. Learning Curve for IBMFullPredict

More Related