Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation

Weakly-supervised text classification aims to induce text classifiers from only a few user-provided seed words. The vast majority of previous work assumes high-quality seed words are given. However, the expert-annotated seed words are sometimes non-trivial to come up with. Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words' efficacy, making the seed word selection process a walk in the dark''. In this work, we remove the need for expert-curated seed words by first mining (noisy) candidate seed words associated with the category names. We then train interim models with individual candidate seed words. Lastly, we estimate the interim models' error rate in an unsupervised manner. The seed words that yield the lowest estimated error rates are added to the final seed word set. A comprehensive evaluation of six binary classification tasks on four popular datasets demonstrates that the proposed method outperforms a baseline using only category name seed words and obtained comparable performance as a counterpart using expert-annotated seed words.

Analyzing and modeling the errors of motion transmission of timing belt drive and correction method for behavior improvement

This paper focuses on the estimation schemes of a viable position for timing belt drives where the position of the carriage (load) is to be determined via reference models receiving input from a position sensor attached to the actuator of the timing belt. A detailed analysis of the transmission error sources is presented, and a number of relevant mathematical models is developed using a priori knowledge of the process. This paper demonstrates that such schemes are very effective when the drive system is not subjected to external loads and operating conditions do not change considerably i.e. ambient temperature, and belt tension.