Abstract: A technique manages collaborative web sessions (CWS). The technique receives graphical content of a CWS. The technique translates a set of portions of the graphical content into text output. The technique provides the text output to a set of text application services. The set of text application services associate the text output with the CWS.

Abstract: Techniques are provided to allow a user to interact with a computer to automatically analyze a transcript and provide interactive feedback pertaining to interactions between the user and other parties. This may be accomplished by dividing the transcript into text sequences, such as sentences, and matching each text sequence against a set of rules that define patterns that relate text sequences to particular characteristic categories. These matches can be further scored and ranked to allow particular text sequences to be interactively displayed to the user in response to selection of a particular categorization.

Abstract: A technique manages collaborative web sessions (CWS). The technique receives graphical content of a CWS. The technique translates a set of portions of the graphical content into text output. The technique provides the text output to a set of text application services. The set of text application services associate the text output with the CWS.

Abstract: Techniques for performing key frame extraction, recording, and navigation in collaborative video presentations. The techniques include extracting a plurality of frames from media content at a predetermined rate, removing frame areas that do not correspond to a screen area for displaying electronic meeting/webinar content, de-duplicating the plurality of frames, identifying frames that correspond to the “slide type” or similar type of frames, and extracting key frames from the slide type of frames. The key frames can be recorded in a slide deck or other similar collection of key frames, as well as displayed as clickable thumbnails in a UI. By clicking or otherwise selecting a thumbnail representation of a selected key frame in the UI, or clicking-and-dragging a handle of a key frame locator bar to navigate the thumbnails to the selected key frame, users can quickly and more efficiently access desired slide presentation content from an electronic meeting/webinar.

Abstract: Techniques for performing key frame extraction, recording, and navigation in collaborative video presentations. The techniques include extracting a plurality of frames from media content at a predetermined rate, removing frame areas that do not correspond to a screen area for displaying electronic meeting/webinar content, de-duplicating the plurality of frames, identifying frames that correspond to the “slide type” or similar type of frames, and extracting key frames from the slide type of frames. The key frames can be recorded in a slide deck or other similar collection of key frames, as well as displayed as clickable thumbnails in a UI. By clicking or otherwise selecting a thumbnail representation of a selected key frame in the UI, or clicking-and-dragging a handle of a key frame locator bar to navigate the thumbnails to the selected key frame, users can quickly and more efficiently access desired slide presentation content from an electronic meeting/webinar.

Abstract: Techniques for real-time generation and customization of text classification models. An initial dataset of input text samples are manually assigned labels, and the labeled input text samples are tokenized and provided as training data to train machine learning classifiers for various classes or categories of the input text samples. As the machine learning classifiers train with the training data, feedback in the form of suggestions (or predictions) are provided in real time by the text classification models regarding which label(s) to assign to any input text sample(s) currently in the training data or any new input text sample(s) further provided as training data for the respective machine learning classifiers. The suggested (or predicted) label(s) can be manually assigned to the input text sample(s), if deemed appropriate, and the newly labeled input text sample(s) can be provided to supplement the existing training data for the respective machine learning classifiers.

Abstract: Technology for generating a consistently labeled training dataset. For each one of multiple previously labeled texts, a distance between the previously labeled text and a current text to be labeled is generated by comparing a list of tokens for the previously labeled text to a list of tokens for the current text to determine an overlap value equal to a number of tokens that match between the list of tokens for the previously labeled text and the list of tokens for the current text, and using the overlap value to calculate a distance between the previously labeled text and the current text that is inversely correlated to the overlap value. Previously labeled texts that are most similar to the current text are identified as those previously labeled texts having the shortest distances to the current text, and are displayed with their previously assigned labels in a label selection user interface.

Abstract: Techniques are provided to allow a user to interact with a computer to automatically analyze a transcript and provide interactive feedback pertaining to interactions between the user and other parties. This may be accomplished by dividing the transcript into text sequences, such as sentences, and matching each text sequence against a set of rules that define patterns that relate text sequences to particular characteristic categories. These matches can be further scored and ranked to allow particular text sequences to be interactively displayed to the user in response to selection of a particular categorization.

Abstract: Techniques for real-time generation and customization of text classification models. An initial dataset of input text samples are manually assigned labels, and the labeled input text samples are tokenized and provided as training data to train machine learning classifiers for various classes or categories of the input text samples. As the machine learning classifiers train with the training data, feedback in the form of suggestions (or predictions) are provided in real time by the text classification models regarding which label(s) to assign to any input text sample(s) currently in the training data or any new input text sample(s) further provided as training data for the respective machine learning classifiers. The suggested (or predicted) label(s) can be manually assigned to the input text sample(s), if deemed appropriate, and the newly labeled input text sample(s) can be provided to supplement the existing training data for the respective machine learning classifiers.

Abstract: Technology for generating a consistently labeled training dataset. For each one of multiple previously labeled texts, a distance between the previously labeled text and a current text to be labeled is generated by comparing a list of tokens for the previously labeled text to a list of tokens for the current text to determine an overlap value equal to a number of tokens that match between the list of tokens for the previously labeled text and the list of tokens for the current text, and using the overlap value to calculate a distance between the previously labeled text and the current text that is inversely correlated to the overlap value. Previously labeled texts that are most similar to the current text are identified as those previously labeled texts having the shortest distances to the current text, and are displayed with their previously assigned labels in a label selection user interface.