Data Programming using Continuous and Quality-Guided Labeling Functions
Table 6: Comparison of Continuous Potentials
['[ITALIC] ψ( [ITALIC] sj, [ITALIC] τj, [ITALIC] y)', '[BOLD] Spouse', '[BOLD] CDR', '[BOLD] Sms', '[BOLD] Dedup', '[BOLD] Ionosphere', '[BOLD] Iris']
We explored many different generic forms of potentials for continuous LFs and we compare them with the specific beta-distribution form. These potentials ψ(sj,τj,y) return a score increasing in sj when there is agreement, and decreasing in sj when there is disagreement (for positive θj): Treat s as a weight: θjyτjsj. Thresholded: θjyτjmax(sj−πj,0) Thresholded Sigmoid: θjyτjsigmoid(sj−πj) Logit (Global conditioning on Beta): θjyτjlogs1−s Half clipped Gaussian instead of Beta with the Gaussian mean at ’1’ when τj and y agree, and at 0 when they disagree. The only learned parameter is then the variance of the Gaussian which we represent as πjy. P(sij|y,lij, kj)=⎧⎪⎨⎪⎩HG(1−sij;%scale=θjy0)if kj=y & lij≠0,HG(sij;scale =θjy1)if kj≠y & lij≠0,1otherwise. (11) We appreciate the difficulty of choosing the right continuous potentials by observing that for most of these other choices the accuracy is really poor.