Abstract: The technology discloses training a classifier to label webpages with categories, extracting from a training database with thousands of webpages tentatively labeled with ground truth categories, dataset A and dataset B; training a classifier using A; and applying it to webpages in B to assign a webpage a label and a classification score. Also disclosed is cleaning B, removing webpages based on evaluation of a decision confidence metric derived from the score assigned for the webpage; and training a second classifier using cleaned B, with second classifier weights initialized independent of trained first classifier weights; and applying the second classifier to webpages in A to assign a webpage the label, score, and decision confidence matrix, and cleaning A, removing webpages from A based on the decision confidence metric. Then combining cleaned A and cleaned B into a combined clean dataset, and training the third classifier using the combined clean dataset.

Abstract: Disclosed technology of training a third classifier to select between sensitive or non-sensitive categories for a webpage including both sensitive and non-sensitive content. The technology involves collecting webpages as a tentatively labeled dataset A and a dataset B, training a first classifier including at least a first sensitive category classifier and a first non-sensitive category classifier using the tentatively labeled dataset A, referring some dual labelled webpages generated by the first classifier to a curator to curate and resolve label conflict, receiving curated labels from the curator and updating dataset B with the curated labels, training a second classifier using the updated dataset B, referring some dual labelled webpages generated by the second classifier to a curator to curate and resolve label conflict, receiving curated labels from the curator and updating dataset A with the curated labels, and training the third classifier with updated dataset A and updated dataset B.

Abstract: The technology discloses training a classifier to label webpages with categories, extracting from a training database with thousands of webpages tentatively labeled with ground truth categories, dataset A and dataset B; training a classifier using A; and applying it to webpages in B to assign a webpage a label and a classification score. Also disclosed is cleaning B, removing webpages based on evaluation of a decision confidence metric derived from the score assigned for the webpage; and training a second classifier using cleaned B, with second classifier weights initialized independent of trained first classifier weights; and applying the second classifier to webpages in A to assign a webpage the label, score, and decision confidence matrix, and cleaning A, removing webpages from A based on the decision confidence metric. Then combining cleaned A and cleaned B into a combined clean dataset, and training the third classifier using the combined clean dataset.

Abstract: Disclosed is technology for choosing between alternative category labels tentatively assigned to tens of thousands of webpages by a classifier ensemble running on processors, applying the classifier ensemble with a sensitive category classifier, a non-sensitive category classifier, a title and metadata classifier and a heuristic classifier to tens of thousands of webpages. Also disclosed is applying a post processor to outputs of the classifier ensemble and tentatively assigning at least two category labels for non-sensitive categories for webpages; two category labels, automatically determining that at least one but not all of the tentatively assigned category labels is a general label and de-assigning the general label; saving the category label that is not de-selected to the webpage; and distributing the assigned category labels for at least some of the tens of thousands of webpages for use in controlling access to webpages by users on user systems protected using the assigned labels.

Abstract: Disclosed are polymorph of allisartan isoproxil and a pharmaceutical composition thereof. The polymorph is non-electrostatic, highly flowable and highly stable, and can be used for treating hypertension and complications thereof.

Abstract: The present invention relates to an amorphous allisartan isoproxil which is an angiotensin II receptor blocker with good flowability and a large bulk density and with no obvious electrostatic phenomenon; the find of the amorphous allisartan isoproxil can effectively reduce the generation of dust pollution during the production, thereby providing convenience the production of allisartan isoproxil. The present invention also relates to a pharmaceutical composition containing the amorphous allisartan isoproxil. The pharmaceutical composition is highly stable, and allisartan isoproxil formulation prepared from the pharmaceutical composition can effectively ensure the quality of the drug.

Abstract: Disclosed are polymorph of allisartan isoproxil and a pharmaceutical composition thereof. The polymorph is non-electrostatic, highly flowable and highly stable, and can be used for treating hypertension and complications thereof.

Abstract: The present invention relates to an amorphous allisartan isoproxil which is an angiotensin II receptor blocker with good flowability and a large bulk density and with no obvious electrostatic phenomenon; the find of the amorphous allisartan isoproxil can effectively reduce the generation of dust pollution during the production, thereby providing convenience the production of allisartan isoproxil. The present invention also relates to a pharmaceutical composition containing the amorphous allisartan isoproxil. The pharmaceutical composition is highly stable, and allisartan isoproxil formulation prepared from the pharmaceutical composition can effectively ensure the quality of the drug.