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We present a data-driven method to estimate absolute magnitudes for O- andB-type stars from the LAMOST spectra, which we combine with {\it Gaia}parallaxes to infer distance and binarity. The method applies a neural networkmodel trained on stars with precise {\it Gaia} parallax to the spectra andpredicts $K_{\rm s}$-band absolute magnitudes $M_{Ks}$ with a precision of0.25\,mag, which corresponds to a precision of 12\% in spectroscopic distance.For distant stars (e.g. $>5$\,kpc), the inclusion of constraints fromspectroscopic $M_{Ks}$ significantly improves the distance estimates comparedto inferences from {\it Gaia} parallax alone. Our method accommodates foremission line stars by first identifying them via PCA reconstructions and thentreating them separately for the $M_{Ks}$ estimation. We also take into accountunresolved binary/multiple stars, which we identify through deviations in thespectroscopic $M_{Ks}$ from the geometric $M_{Ks}$ inferred from {\it Gaia}parallax. This method of binary identification is particularly efficient forunresolved binaries with near equal-mass components and thus provides an usefulsupplementary way to identify unresolved binary or multiple-star systems. Wepresent a catalog of spectroscopic $M_{Ks}$, extinction, distance, flags foremission lines, and binary classification for 16,002 OB stars from LAMOST DR5.As an illustration of the method, we determine the $M_{Ks}$ and distance to theenigmatic LB-1 system, where Liu et al. (2019) had argued for the presence of ablack hole and incorrect parallax measurement, and we do not find evidence forerrorneous {\it Gaia} parallax.

Data-driven spectroscopic estimates of absolute magnitude, distance and binarity -- method and catalog of 16,002 O- and B-type stars from LAMOST