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The following is an excerpt from the Estimating LAT Confidence section of this paper:

... Since some LAT detection rules are more reliable than others, we would like to have a confidence value in each LAT that can be used to weight each LAT appropriately during answer scoring. To accomplish this, we trained a logistic regression classifier using a manually annotated gold standard. The classifier uses the focus and LAT rules that have fired as features, along with other features from the parse, NER, and the prior probability of a particular word being a LAT...

It's not clear to me what is the target variable of the logistic regression classifier. It sounds to me that it is binary variable indicating whether the fired rule was correct (1) or incorrect (0). Therefore, the prediction of the logistic regression classifier is the probability that the word is an LAT given that a certain rule has been hired (and some other features). Does this application of the logistic regression classifier make sense?

The details of the paper are as follows:

A. Lally et al., "Question analysis: How Watson reads a clue," in IBM Journal of Research and Development, vol. 56, no. 3.4, pp. 2:1-2:14, May-June 2012. doi: 10.1147/JRD.2012.2184637

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  • $\begingroup$ Welcome to CS.SE! Can you edit the question to include a reference for the paper (e.g., title, authors, where published), so that the question remains understandable even if the link stops working, and so that others interested in the paper can find this page via search? $\endgroup$ – D.W. Jul 15 '17 at 5:40
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From context, I'd guess that the logistic regression classifier is used to predict the probability that a particular word is a LAT. This looks equivalent to predicting the probability that the fired rule was correct to flag that word as a LAT. This seems to make sense given the context.

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