A system includes a content extraction engine comprising at least one processor and configured to receive a content page for a target product including product data for the target product and noise content unrelated to the target product, identify noise content pertaining to data unrelated to the target product, remove noise content from the content page, thereby generating a remainder content page containing target product data usable to enable product comparison between multiple sources.

The present disclosure provides an entity relation mining method based on a biomedical literature, including the following steps: querying a disease-associated biomedical literature in a public database, and performing data preprocessing to obtain biomedical text data; performing biomedical named entity recognition on obtained biomedical text data in combination with a regex matching pattern and a deep learning model; and mining an entity relation with transfer learning and reinforcement learning based on an entity recognition result. By acquiring the disease-associated biomedical literature from a network, extracting an abstract and a title and performing entity recognition and relation mining, the present disclosure can effectively recognize biomedical noun entities in the literature and mine potential relations between various entities.

The present disclosure provides an entity relation mining method based on a biomedical literature, including the following steps: querying a disease-associated biomedical literature in a public database, and performing data preprocessing to obtain biomedical text data; performing biomedical named entity recognition on obtained biomedical text data in combination with a regex matching pattern and a deep learning model; and mining an entity relation with transfer learning and reinforcement learning based on an entity recognition result. By acquiring the disease-associated biomedical literature from a network, extracting an abstract and a title and performing entity recognition and relation mining, the present disclosure can effectively recognize biomedical noun entities in the literature and mine potential relations between various entities.

A mechanism is provided in a data processing system to implement a cognitive natural language processing (NLP) system with descriptor uniqueness identification to support named entity mention clustering. The mechanism annotates a set of documents from a corpus of documents for entity types and mentions, collects descriptor usages from all documents in the corpus of documents, analyzes the descriptor usages to classify the descriptors as base terms or modifier terms, generates compatibility scores for the descriptors, and performs entity merging of entity clusters based on the compatibility scores.

A mechanism is provided in a data processing system to implement a cognitive natural language processing (NLP) system with descriptor uniqueness identification to support named entity mention clustering. The mechanism annotates a set of documents from a corpus of documents for entity types and mentions, collects descriptor usages from all documents in the corpus of documents, analyzes the descriptor usages to classify the descriptors as base terms or modifier terms, generates compatibility scores for the descriptors, and performs entity merging of entity clusters based on the compatibility scores.

An employee of a large organization sends a human-readable document such as an email or text message to another employee of the organization to inform the other employee of a change in availability. A trained machine-learning model extracts, from the human-readable document, data used by a leave management system (LMS) to formalize and memorialize the leave request. For example, the employee name, manager name, date leave begins, date leave ends, reason for the leave request, or any suitable combination thereof may be determined by the machine-learning model based on the human-readable document. The extracted data is provided to the LMS and the leave request is created.

An employee of a large organization sends a human-readable document such as an email or text message to another employee of the organization to inform the other employee of a change in availability. A trained machine-learning model extracts, from the human-readable document, data used by a leave management system (LMS) to formalize and memorialize the leave request. For example, the employee name, manager name, date leave begins, date leave ends, reason for the leave request, or any suitable combination thereof may be determined by the machine-learning model based on the human-readable document. The extracted data is provided to the LMS and the leave request is created.

Sentiment classification can be implemented by an entity-level multimodal sentiment classification neural network. The neural network can include left, right, and target entity subnetworks. The neural network can further include an image network that generates representation data that is combined and weighted with data output by the left, right, and target entity subnetworks to output a sentiment classification for an entity included in a network post.

A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.

Embodiments of the disclosure provide systems and methods for processing unstructured texts in a medical record. A disclosed system includes at least one processor configured to determine a plurality of word representations of an unstructured text and tag entities in the unstructured text by performing a named entity recognition task on the plurality of word representations. The at least one processor is further configured to determine position embeddings based on positions of words in the unstructured text relative to positions of the tagged entities and concatenate the plurality of word representations with the position embeddings. The at least one processor is also configured to determine relation labels between pairs of tagged entities by performing a relationship extraction task on the concatenated word representations and position embeddings.