Abstract: Software architectures relating to machine learning (e.g., relating to classifying sequential text data. Unlabeled sequential text data may be produced by a variety of sources such as text messages, email messages, message chats, social media applications, and web pages. Classifying such data may be difficult due to the freeform and unlabeled nature of text data from these sources. Thus, techniques for training a machine learning algorithm to classify unlabeled text data in freeform format. Training is based on generation of labelling functions from lexical databases, applying the labelling functions to unlabeled text data in an unsupervised manner, and generating trained classifiers that accurately classify the unlabeled text data. The trained classifiers may then be implemented classify text data accessed from the variety of sources. The present techniques provide high-quality and efficient labeling of unlabeled text data in freeform formats.

Abstract: A composition contains ?-glucan and is used for preventing and/or treating hormone-dependent dermatitis, skin mucositis or other skin inflammatory diseases.

Abstract: Provided are a biological polysaccharide having the effect of preventing and treating acne and/or steroid-dependent dermatitis and the use thereof. Specifically, provided is the use of ?-glucan for preparing a formulation or composition, wherein the formulation or composition is used in the prevention and/or treatment of acne and/or steroid-dependent dermatitis. The ?-glucan has the effects of significantly relieving and improving acne and/or steroid-dependent dermatitis, effectively controlling the occurrence of acne and/or steroid-dependent dermatitis, accelerating the healing and/or regression of acne and/or steroid-dependent dermatitis.