A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform using a plugin system in a user interface to identify each ingredient in an ingredient list of a recipe published on a webpage shown on the user interface; identifying query strings from content on the webpage associated with one or more ingredients of the recipe; identifying one or more respective recipe products and a respective quantity for each of the one or more ingredients; locating a respective catalog product in an online catalog for each of the one or more respective recipe products; automatically generating a list of catalog products; automatically generating a link comprising the list of catalog products; automatically redirecting the user interface to an online retail website; and automatically adding the list of catalog products to an electronic shopping cart. Other embodiments are disclosed.

A system retrains a natural language understanding (NLU) model by regularly analyzing electronic documents including web publications such as online newspapers, blogs, social media posts, etc. to understand how word and phrase usage is evolving. Generally, the system determines the frequency of words and phrases in the electronic documents and updates an NLU dictionary depending on whether certain words or phrases are being used more frequently or less frequently. This dictionary is then used to retrain the NLU model, which is then applied to predict the meaning of text or speech communicated by a people group. By analyzing electronic documents such as web publications, the system is able to stay up-to-date on the vocabulary of the people group and make correct predictions as the vocabulary changes (e.g., due to natural disaster). In this manner, the safety of the people is improved.

A system retrains a natural language understanding (NLU) model by regularly analyzing electronic documents including web publications such as online newspapers, blogs, social media posts, etc. to understand how word and phrase usage is evolving. Generally, the system determines the frequency of words and phrases in the electronic documents and updates an NLU dictionary depending on whether certain words or phrases are being used more frequently or less frequently. This dictionary is then used to retrain the NLU model, which is then applied to predict the meaning of text or speech communicated by a people group. By analyzing electronic documents such as web publications, the system is able to stay up-to-date on the vocabulary of the people group and make correct predictions as the vocabulary changes (e.g., due to natural disaster). In this manner, the safety of the people is improved.

A platform receives an input document from a user device and automatically determines a semantic signature for the input document based on a probabilistic distribution of rare words within the input document. The platform automatically scrapes at least one Internet database for additional documents and webpages, determining semantic signatures for each document or webpage. Based on similarity of semantic signatures, the platform automatically constructs and displays a graphical network of documents, wherein each document is represented as a node and similarity of semantic signatures is used to determine the locations of edges between nodes. The graph automatically groups nodes by communities and selects nodes in different communities to promote serendipity of results.

Various embodiments may be generally directed to the use of an adversarial learning framework for persona-based dialogue modeling. In some embodiments, automated multi-turn dialogue response generation may be performed using a persona-based hierarchical recurrent encoder-decoder-based generative adversarial network (phredGAN). Such a phredGAN may feature a persona-based hierarchical recurrent encoder-decoder (PHRED) generator and a conditional discriminator. In some embodiments, the conditional discriminator may include an adversarial discriminator that is provided with attribute representations as inputs. In some other embodiments, the conditional discriminator may include an attribute discriminator, and attribute representations may be handled as targets of the attribute discriminator. The embodiments are not limited in this context.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to effectively and efficiently generate one or more abstractive summaries of one or more multi-section documents. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an abstractive summary of a multi-section document comprising one or more sections, by generating one or more section summaries, section input batches for each selected section, model outputs created by one or more text summarization machine learning models through the performance of a batch processing operation sequence, abstractive summaries, and then storing the abstractive summaries.

A system and a method for training an inference model using a computing device. The method includes: providing a text-to-vector converter; providing the inference model and pre-training the inference model using labeled fashion entries; providing non-labeled fashion entries; separating each of the non-labeled fashion entries into a target image and target text; converting the target text into a category vector and an attribute vector using the text-to-vector converter; processing the target image using the inference model to obtain processed target image and target image label; comparing the category vector to the target image label; when the category vector matches the target image label, updating the target image label based on the category vector and the attribute vector to obtain updated label; and retraining the inference model using the processed target image and the updated label.

A system and a method for training an inference model using a computing device. The method includes: providing a text-to-vector converter; providing the inference model and pre-training the inference model using labeled fashion entries; providing non-labeled fashion entries; separating each of the non-labeled fashion entries into a target image and target text; converting the target text into a category vector and an attribute vector using the text-to-vector converter; processing the target image using the inference model to obtain processed target image and target image label; comparing the category vector to the target image label; when the category vector matches the target image label, updating the target image label based on the category vector and the attribute vector to obtain updated label; and retraining the inference model using the processed target image and the updated label.

Provided are a text classification method, an electronic device, and a computer-readable storage medium. The method includes acquiring the to-be-tested text; detecting a sensitive word through an AC automaton to determine whether the to-be-tested text contains the sensitive word; and in response to a determination result that the to-be-tested text contains the sensitive word, determining the text category of the to-be-tested text based on the sensitive word contained in the to-be-tested text.

An information processing apparatus includes a processor. The processor receives an input of a graph structure. The graph structure has nodes including text and edge. The processor assigns the nodes to one or more clusters. The processor partitions the text into words. The processor classifies the words into 1) a word representing a subject or target of an operation, 2) a word representing a content or state of the operation, and 3) other words. The processor extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the subject or target of the operation and extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the content or state of the operation, for the respective clusters.