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Building a Maxent Model

Building a Maxent Model. The nuts and bolts. Building a Maxent Model. We define features (indicator functions) over data points Features represent sets of data points which are distinctive enough to deserve model parameters.

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Building a Maxent Model

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  1. Building a Maxent Model The nuts and bolts

  2. Building a Maxent Model • We define features (indicator functions) over data points • Features represent sets of data points which are distinctive enough to deserve model parameters. • Words, but also “word contains number”, “word ends with ing”, etc. • We will simply encode each Φfeature as a unique String • A datum will give rise to a set of Strings: the active Φfeatures • Each feature fi(c, d)  [Φ(d) c=cj]gets a real number weight • We concentrate on Φfeatures but the math uses iindices of fi

  3. Building a Maxent Model • Features are often added during model development to target errors • Often, the easiest thing to think of are features that mark bad combinations • Then, for any given feature weights, we want to be able to calculate: • Data conditional likelihood • Derivative of the likelihood wrt each feature weight • Uses expectations of each feature according to the model • We can then find the optimum feature weights (discussed later).

  4. Building a Maxent Model The nuts and bolts

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