Abstract: A method, an electronic device and computer readable medium for entity-relationship embeddings using automatically generated entity graphs instead of a traditional knowledge graph are provided. The method includes receiving, by a processor, an input text. The method also includes identifying a primary entity, a secondary entity and a context from the input text, wherein the context comprises a relationship between the primary entity and the secondary entity. The method additionally includes generating, by the processor, an entity context graph based on the primary entity, the secondary entity, and the context by: extracting, from the context, one or more text segments comprising a plurality of words describing one or more additional relationships between the primary entity and the secondary entity, and generating a plurality of context triples from the one or more text segments, each of the plurality of context triples defining a respective relationship between primary entity and the secondary entity.

Abstract: Word sense disambiguation using a glossary layer embedded in a deep neural network includes receiving, by one or more processors, input sentences including a plurality of words. At least two words in the plurality of words are homonyms. The one or more processors convert the plurality of words associated with each input sentence into a first vector including possible senses for the at least two words. The first vector is then combined with a second vector including a domain-specific contextual vector associated with the at least two words. The combination of the first vector with the second vector is fed into a recurrent deep logico-neural network model to generate a third vector that includes word senses for the at least two words. A threshold is set for the third vector to generate a fourth vector including a final word sense vector for the at least two words.

Abstract: An information processing apparatus includes a lexical analysis unit that generates a training word string, a group generation unit that generates a plurality of training word groups, a matrix generation unit that generates, for each training word group, a training matrix in which a plurality of words and respective semantic vectors of the words are associated, a classification unit that calculates, for a word of each position of the training word string, a probability of the word corresponding to a specific word, using the training matrices generated by the matrix generation unit and a determination model that uses a convolutional neural network, and an optimization processing unit that updates parameters of the determination model, such that the probability of the word labeled as corresponding to the specific word is high, among the probabilities of the words of the respective positions of the training word string calculated by the classification unit.

Abstract: The present disclosure in some embodiments provides a dialogue state tracking method and a dialogue state tracking apparatus in a goal-oriented dialogue system, which track a dialogue state through training based on an attention mechanism between utterances and contextual semantic vectors corresponding respectively to domain-slot types and based on a distance metric-based non-parametric method and thereby facilitate service extension to a multi-domain scenario in the dialogue system.

Abstract: Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Abstract: Methods enable creation of a game slice from a game. Plurality of games is provided for presentation on a display device. Each game is identified by an image. Selection activity is detected at the image of one of the games. In response to the selection, game code of the selected game is executed to enable game play of an unlocked game. The selected game is streamed to the display device. User interaction related to the game play is received. A recording of the game play is examined to identify portions of the game for generating a game slice, which are returned in a suggested list for selection. Game slice is generated for a selected portion from the list. A recording of the game play for the game slice is associated as a primary video segment. The game slice and the primary video segment are provided for sharing over a network.

Abstract: A language sequence labeling method and includes: reading a first embedding representation of a language sequence, the first embedding representation including a character-level word embedding representation, a pre-trained word embedding representation, and a global word embedding representation of the language sequence, the global word embedding representation referring to a global context representation of the language sequence; performing first depth transformation (DT) encoding on the first embedding representation based on a first DT recurrent neural network (RNN), to output a first hidden-layer state representation corresponding to each word in the language sequence; and decoding the first hidden-layer state representations of the language sequence, to obtain a labeling result of one or more elements in the language sequence.

Abstract: A method for denormalizing raw speech recognition results. The method includes receiving a speech input from a user and obtaining context metadata associated with the speech input. The context metadata indicates that the speech input includes dictated speech directed to a messaging application that is currently executing on a user device for inclusion in an electronic message. The method further includes generating, using a speech recognizer, a raw speech recognition result including an explicit punctuation term spoken by the user and corresponding to the speech input. Based on the context metadata, the method includes denormalizing the generated raw speech recognition result into denormalized text by applying an explicit punctuation denormalizer to convert the explicit punctuation term in the raw speech recognition result into a corresponding punctuation symbol and displaying the denormalized text including the corresponding punctuation symbol on a display screen of the user device.

Abstract: A system and method for creating input data to be used to train a conversational bot may include receiving a set of conversations, each conversation including sentences, classifying each sentence into a dialog act taken from a number of dialog acts, for each set of sentences classified into a dialog act, clustering the set of sentences into clusters based on the content (e.g. text) of the sentences, each cluster having a cluster name or label, and generating a language model based on the cluster labels. Slots may be identified in the sentences based in part on the dialog act classifications. A bot may be trained using data such as the slots, language model, and clusters.

Abstract: A computer-implemented system and method for searching comprises, in an initial training phase, storing a plurality of user journeys in a stored journey database (SJDB), and storing a plurality of intents in a stored intents database (SIDB). The plurality of user journeys comprise a plurality of intents linked together by weighted links. In a first use phase, the method comprises determining a first intent of a first user or a second user, and modifying a first weighting of the weightings based on the first intent. In a second use phase, the method comprises receiving, from the second user, a second user question, determining a second intent from the second user question, and locating a user journey from the plurality of user journeys in the SJDB related to the second intent.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting a continued conversation are disclosed. In one aspect, a method includes the actions of receiving first audio data of a first utterance. The actions further include obtaining a first transcription of the first utterance. The actions further include receiving second audio data of a second utterance. The actions further include obtaining a second transcription of the second utterance. The actions further include determining whether the second utterance includes a query directed to a query processing system based on analysis of the second transcription and the first transcription or a response to the first query. The actions further include configuring the data routing component to provide the second transcription of the second utterance to the query processing system as a second query or bypass routing the second transcription.

Abstract: An apparatus comprises at least one processing device configured to receive a query to perform sentiment analysis for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying sentiment of the words of the document, and to determine, for a given aspect term, attention weights for a given subset of the words of the document surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, a given sentiment classification of the given aspect term based on the attention weights and a given portion of the second set of encodings for the given subset of the words, and to provide a response to the query comprising the given sentiment classification.

Abstract: An automated support system for local networks of connected devices has local network agents each comprising a digital processor, a memory, a local communications wireless interface for communicating locally with devices in the local network, and remotely with cloud-based servers. The servers include an interface for communicating with the local networks, and a recommendation engine programmed to dynamically communicate with the local network agents via the interface to capture support requests concerning devices and services, to perform work flow processing to generate a resolution for such support requests, and to communicate the resolution to the local network agent. The recommendation engine automatically generates a context for each support request, the context including historical resolution data for that local network, or device, or service.

Abstract: A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.

Abstract: In some cases, lower quality, large scale training data can be automatically generated by automatic labeling. The generated training data can be used to pre-train a machine learning model. For instance, the model can be a model for detection of verbal harassment. Parameters of the pre-trained model can be refined or updated using another one or more higher-quality sets of training data, with which the model can be subsequently trained.

Abstract: A computer-implemented technique generates a dense embedding vector that provides a distributed representation of input text. The technique includes: generating an input term-frequency (TF) vector of dimension g that includes frequency information relating to frequency of occurrence of terms in an instance of input text; using a TF-modifying component to modify the term-specific frequency information in the input TF vector by respective machine-trained weighting factors, to produce an intermediate vector of dimension g; using a projection component to project the intermediate vector of dimension g into an embedding vector of dimension k, where k is less than g. Both the TF-modifying component and the projection component use respective machine-trained neural networks. An application performs any of a retrieval-based function, a recognition-based function, a recommendation-based function, a classification-based function, etc. based on the embedding vector.

Abstract: Systems and methods are configured to determine customer-specific recommendations in the form of ordered listings of objects (e.g., talking points for discussion) for interacting with customers during customer-service interactions based on the effectiveness of historical customer-service interactions. One or more machine-learning customer similarity models are further configured to determine similarities between customers, such that customer-service interaction strategies utilized for a first customer may be applied to determined similar customers. Moreover, based at least in part on historical interaction data generated for previous customer-service interactions, a machine-learning recommendation model is configured to generate an ordered listing of objects (e.g., talking points for discussion) for interacting with customers, and such recommendations may be presented to a customer-service representative via a graphical user interface.

Abstract: A primary chatbot may receive a query from a user. The primary chatbot, using natural language processing techniques, may analyze the query. The primary chatbot may identify, from the analyzing, one or more key features of the query. The primary chatbot may push the one or more key features to one or more secondary chatbots. The primary chatbot may identify which one of the primary chatbot and the one or more secondary chatbots is to respond to the query. The primary chatbot may transmit the response to the user.

Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.

Abstract: A system and method for creating customized, personally relevant greeting cards, e-cards, mementos, invitations, decorations, and other types of printed and virtual media to convey affection, friendship, emotional connections, celebration, gratitude, condolence and other types of sentiments to relatives, friends, coworkers, business associates, and acquaintances regardless of the cultural backgrounds of the giver and the recipient.

Abstract: Systems and methods for automatic generation of free-form conversational interfaces are disclosed. In one embodiment, a system receives an input from a user device through a conversational graphical user interface (GUI). An intent of the user may be determined based on the received input. Based on the intent of the user, the system may identify, from a plurality of objects available to the system, one or more objects. Each of the plurality of objects has annotations corresponding to one or more elements of the object and one or more functions of the object. The one or more functions corresponding to the one or more elements are executable to perform an action upon corresponding elements. Based on the identified one or more objects and the annotations of the identified one or more objects, the system may generate a dynamic dialogue flow for the conversational GUI, where the dynamic dialogue flow is generated in real-time during a conversational GUI session.

Abstract: Systems and methods for generating a tuned summary using a word generation model. An example method includes receiving, at a decoder of the word generation model, a training data learned subspace representation of training data. The method also includes identifying tunable linguistic characteristics of the word generation model and training the decoder to output a training tuned summary of the training data learned subspace representation based on at least one of the tunable linguistic characteristics. The method further includes receiving an input text and a target characteristic token, and generating, by the trained decoder of the word generation model, each word of a tuned summary of the input text from a learned subspace representation and from feedback about preceding words of the tuned summary, wherein the tuned summary is tuned to target characteristics represented by the target characteristic token.

Abstract: An information output system includes a speech acquisition unit configured to acquire a speech of a user, a recognition processing unit configured to recognize the content of the acquired speech of the user, and an output processing unit configured to output a question to the user and to perform processing for outputting a response to the content of the speech of the user who has answered the question. The output processing unit is configured to derive a user's positive degree based on the content of the speech of the user who has answered the question and to determine guidance information to be output to the user based on the derived positive degree.

Abstract: An automated tool analyzes source code repositories and web endpoints for unique characteristics that they both share in order to predict the likelihood that a particular source code repository contains source code files used in a web endpoint and to predict the likelihood that a web endpoint uses source code files of a particular repository. The unique characteristics are referred to as fingerprints and include unique combination of public-facing entities, unique tokens, and unique DOM characteristics.

Abstract: A contact center, communication system, and method of operating a contact center are provided. In one example, the contact center includes a tunable chatbot stored in memory and a chatbot tuning function that controls and adjusts a personality characteristic of the tunable chatbot while the tunable chatbot is interacting with a customer via a customer communication device.

Abstract: A data processing system receives a plurality of electronic documents in image format. The system extracts text from the electronic documents using an optical character recognition processor, and determines a plurality of entity data based on the extracted text. The system receives pre-defined question data from a user, and determines pre-annotated answer data based on the entity data and the question data using an open-domain question answering model. The system determines context data based on the entity data and the question data. The system provides the pre-annotated answer data to the user, and receives corrected entity data from the user. The system trains a closed-domain question answering model based on the corrected entity data and re-aligned context data. The system determines a plurality of n-gram words based on the corrected entity data and the context data using a context phrase model. The n-grams words are stored in a knowledge base.

Abstract: The present disclosure provides a method, a device, an equipment and a storage medium for mining a topic concept. The method includes: acquiring a plurality of candidate topic concepts based on a query; performing word segmentation on the plurality of candidate topic concepts and performing part-of-speech tagging on words obtained after performing the word segmentation, to obtain a part-of-speech sequence of each of the plurality of candidate topic concepts; and filtering the plurality of candidate topic concepts based on the part-of-speech sequence, to filter out a topic concept corresponding to a target part-of-speech sequence among the plurality of candidate topic concepts, in which a proportion of accurate topic concepts in the target part-of-speech sequence is lower than or equal to a first preset threshold, or a proportion of inaccurate topic concepts in the target part-of-speech sequence is higher than or equal to a second preset threshold.

Abstract: A computer-implemented method, a computer system, and a computer program product for automatically generated conversation output is provided. The present invention may include training an answer-intent classifier to associate an intent expressed in an example question with an example answer that is responsive to the example question. The present invention may further include classifying, using the trained answer-intent classifier, a content transmitted to the trained answer-intent classifier with the intent expressed in the example question in response to determining, using the trained answer-intent classifier, that the content includes relevant information for generating the example answer that is responsive to the example question.

Abstract: A system performs conversations with users using chatbots customized for performing a set of tasks. The system may be a multi-tenant system that allows customization of the chatbots for each tenant. The system receives a task configuration that maps tasks to entity types and an entity configuration that specifies methods for determining entities of a particular entity type. The system receives a user utterance and determines the intent of the user using an intent detection model, for example, a neural network. The intent represents a task that the user is requesting. The system determines one or more entities corresponding to the task. The system performs tasks based on the determined intent and the entities and performs conversations with users based on the tasks.

Abstract: Text is received from a first user. The text is associated with an electronic communication tool for communication to a second user. Candidate answers are generated based on the text using a question answering system. At least one of the candidate answers is provided to the first user.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: Methods, systems, and computer program products for data filtering with fuzzy attribute association are provided herein. A computer-implemented method includes obtaining one or more rules, specified by an expert, that define a partial ranking of a plurality of fuzzy pairings between (i) a plurality of item attributes for items in a data catalog and (ii) a plurality of user attributes related to said items; generating an interactive session with the expert to resolve one or more ambiguities in the one or more rules; and deriving a scoring function based at least in part on (i) the one or more rules and (ii) the resolved one or more ambiguities, wherein the scoring function generates a comparative score between any two items of said data catalog for a given one of the users associated with the plurality of attributes.

Abstract: A computer-implemented method for text conversion, a computer device, and a non-transitory computer readable storage medium are provided. The method includes: obtaining a text to be converted; performing a non-standard word recognition on the text to be converted, to determine whether the text to be converted includes a non-standard word; recognizing the non-standard word in the text to be converted by using an eXtreme Gradient Boosting model in response to the text to be converted including the non-standard word; and obtaining a target converted text corresponding to the text to be converted, according to a recognition result outputted by the eXtreme Gradient Boosting model. The method has a faster recognition speed and a higher recognition accuracy compared with the deep learning model.

Abstract: There are provided systems and methods for enabling user to user interactions in multi-user video conference applications. A user may utilize a computing device to interact with online service providers in order to initiate and/or join a video conference with one or more other users. During the video conference, the user may be provided with options to interact with other users, such as by engaging in electronic transaction processing. When performing such interactions, data for the video conference may be extracted and provided to a transaction processor server. The server may then utilize the data to determine accounts of users within the video conference that may be utilized for electronic transaction processing services. If no account exists, the server may initiate a process to onboard such users. When an account exists, the server may further populate graphical icons that may be used for electronic transaction processing services.

Abstract: Aspects of the present disclosure relate to automatically generating a user manual using a technique that includes training a first model with a first set of training data. The technique further includes generating, by the first model, a set of operations and a set of windows, where the set of operations and the set of windows are functions of the program. The technique further includes, generating a plurality of tasks, where a first task comprises a first operation being performed on a first window. The technique further includes determining an order of the plurality of tasks and calculating a level score for the first operation of the first window. The technique further includes assembling the user manual having the plurality of tasks in the determined order.

Abstract: Techniques for computer-generated conversation are disclosed. In an example, a method identifies text postings from a conversation. The method creates, for each text fragment of each text posting, a syntactic tree and a discourse tree. The method creates parse thickets, each parse thicket including the syntactic tree and discourse tree of a unique pair of text postings. The method extracts, from each parse thicket, a common text segment and obtains a set of candidate search results by providing the common text segments to a search engine. The candidate search results can be further refined for relevancy and mental state and posted as a response to a conversation.

Abstract: A method in a data processing system comprising a processor and a memory, for processing data entries, the method comprising receiving, by the data processing system, a data entry, parsing, by the data processing system, the data entry for features by using natural language processing (NLP), identifying, by the data processing system, data sets from a corpus of information that are relevant to the data entry, and linking, by the data processing system, the identified data sets to the data entry.

Abstract: A method and neural network system for human-computer interaction, and user equipment are disclosed. According to the method for human-computer interaction, a natural language question and a knowledge base are vectorized, and an intermediate result vector that is based on the knowledge base and that represents a similarity between a natural language question and a knowledge base answer is obtained by means of vector calculation, and then a fact-based correct natural language answer is obtained by means of calculation according to the question vector and the intermediate result vector. By means of this method, a dialog and knowledge base-based question-answering are combined by means of vector calculation, so that natural language interaction can be performed with a user, and a fact-based correct natural language answer can be given according to the knowledge base.

Abstract: An electronic apparatus and a controlling method thereof are provided. The method of controlling an electronic apparatus according to an embodiment includes: obtaining first text information including a plurality of words, identifying a security level of the first text information based on at least one of a source from which the first text information is obtained or a type of the first text information, obtaining second text information by converting at least one of the plurality of words included in the first text information based on the identified security level, and obtaining first summary sentence information summarizing the second text information through a summary module.

Abstract: In one aspect, the present disclosure relates to a method which, in one example embodiment, can include receiving text data that includes at least unstructured data and wherein the text data is associated with a plurality of messages communicated between a plurality of entities. The method can also include determining relationships between the entities, based on the text data associated with the plurality of messages, and generating, from a knowledge base assembled from at least the text data, a response to a user interaction representing a query for information that corresponds to at least one of the entities and indicates information on one or more of the determined relationships between the entities. The method can also include detecting a deviation in communication between the entities that indicates unauthorized disclosure of information between a first entity and a second entity.

Abstract: Disclosed are systems and methods for translation comments flowback. In some embodiments, the method includes the steps of: obtaining a first document associated with a primary document, the primary document in a primary language, the first document comprising one or more translated sections in a first language, the one or more translated sections being mapped to one or more sections in the primary document via a content identifier, the first language being different from the primary language; transmitting the first document to a first user for review; receiving a first input associated with the one or more translated sections in the first document from the first user; and populating the first input to the primary document based on the content identifier.

Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.

Abstract: Techniques to apply machine learning to schedule events of interest for a device user. As described herein, a typical device user is inundated with information suggesting activities and other things to do. Using these techniques, the information is categorized according to the activity and those activities that are most likely to be engaged in by the device user are recommended to the device user (via their device) as events of interest. If the device user selects an event of interest, the device is updated to reflect that selection. For instance, a calendar application may be updated to include an event description at the event's date and time. Other embodiments are described and claimed.

Abstract: An editor service receives a textual input. The editor service provides the textual input to a rule-based grammar checker to obtain a grammar checker result. The editor service also provides the textual input to a machine learning (ML) fluency model that checks the textual input for errors and provides a ML model result. The editor service aggregates the grammar checker result and the ML model result and generates an editor service output based upon the aggregated results. A representation of the editor service result is provided to the client computing system for surfacing through a user interface.

Abstract: Systems and methods are disclosed for utilizing a comment moderation bot for detecting and normalizing neologisms in social media. One method comprises transmitting, by a neologism normalization system, a comment moderation bot for detecting neologisms on an online platform maintained by one or more publisher systems. The comment moderation bot may aggregate data related to user comments and transmit the aggregated data to the neologism normalization system for further processing. The neologism normalization system implements unsupervised machine learning models for detecting neologisms in the aggregated data through tokenization and filtering; and normalizing the neologisms through similarity analysis and lattice decoding.

Abstract: A method, computer program product, and a system where a processor(s) ingests content from a source(s) with an attribute(s) comprising a verified temporal context(s) of the source(s). The processor(s) cognitively analyzes the content, by applying an entity recognition algorithm(s) to identify and extract entities in the source(s). The processor(s) classifies each extracted entity into a given grouping from a plurality of groupings based on at least one attribute comprising the verified temporal context of the source from which the extracted entity was extracted. The processor(s) generates a corpus comprising the groupings; each grouping comprises extracted entities with verified temporal contexts consistent with a defined time period.

Abstract: A method may include extracting training tokens from a training corpus, generating training token features and training contextual features for each of the training tokens, training a probabilistic graphical model using classes, the training token features, and the training contextual features, extracting, from actual data, actual tokens including an actual token, generating actual token features and actual contextual features for each of the actual tokens, and classifying the actual token as a class using the probabilistic graphical model, the actual token features, and the actual contextual features.

Abstract: One embodiment provides a method, including: obtaining at least two documents, wherein one of the at least two documents comprises a revision different than another of the at least two documents; identifying, within each of the at least two documents, portions corresponding to groups of text containing a conceptual unit; assigning at least a subset of the identified portions to a category type corresponding to a topic of a given portion, wherein the assigning comprises (i) generating a semantic tag for the identified portions in the subset and (ii) tagging the identified portions in the subset with the semantic tag; and determining changes between the at least two documents, wherein the determining comprises (iii) aligning given portions across the at least two documents based upon a relationship between the given portions across the at least two documents, (iv) identifying semantic differences between the aligned portions, and (v) identifying any remaining unaligned portions.

Abstract: Systems and methods to briefly deviate from and resume back to amending a section of a note are disclosed. Exemplary implementations may: obtain audio information representing sound captured by an audio section of a client computing platform, such sound including speech from a user associated with the client computing platform; effectuate presentation of a graphical user interface that includes sections of the note; analyze the audio information to determine which individual ones of the spoken inputs are the primary spoken input or the deviant spoken input; determine, based on analysis, which section of the note to which the deviant spoken input is related; alternately amend, based on the determination, sections of the note by deviating from one section to another section and returning back to the one section for continued population; and effectuate, via the user interface, presentation of the alternating amendments to the sections of the note.