Luminoso’s new deep learning model understands documents using multiple layers of attention, a mechanism that identifies which words are relevant to get context around a specific concept as expressed by a word or phrase. This model is capable of identifying the author’s sentiment for each individual concept they’ve written about, as opposed to providing an analysis of the overall sentiment of the document.
Using Concept-Level Sentiment, users will be able to:
- Effectively analyze mixed feedback — Concept-level sentiment analysis is critical for capturing and understanding the voice of the customer (VoC). For example, product reviews rarely contain just one type of feedback, and it’s important to tease apart the good from the bad. Getting a polarity for each of the topics in an open-ended survey response is critical for understanding what works and what doesn’t for your customers.
- Quickly surface buried feedback — Uncovering negative comments in overwhelmingly positive open-ended survey responses is critical for better understanding customers and employees. For instance, in voice of the employee (VoE) surveys, employee feedback can be overwhelmingly positive and delivered in an upbeat way in an effort to soften criticisms. Concept-Level Sentiment in Luminoso enables users to quickly identify and understand “buried” feedback, such as negative points in an overwhelmingly positive HR survey.
- Intuitively aggregate concept sentiment across an entire dataset — For instance, after responses to a mobile app market research survey are loaded into Luminoso Daylight, a user can get a distribution of positive, negative, and neutral opinions about every aspect of the mobile experience across all of its mentions in the dataset.
- Analyze customer and employee feedback across multiple languages — Global organizations often receive customer and employee feedback in multiple languages. With Luminoso, users can analyze the sentiment of concepts, natively in 15 languages.
https://luminoso.com/solutions/concept-level-sentiment
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video could be seen as information extraction.
Cortical.io announced a new release of Cortical.io Contract Intelligence, an AI-based solution for enterprises that need to review and manage a large corpus of contracts and other legal documents. Cortical.io Contract Intelligence utilizes its natural language understanding (NLU) to automatically extract, classify and analyze relevant information in documents. Cortical.io Contract Intelligence 4.0 now incorporates semantic search to enable the search of an entire database or individual documents.
Other features in Cortical.io Contract Intelligence 4.0 are integration with other Business Intelligence solutions and workflow improvements. These include:
- A dashboard to enable specialists to manage and track the review progress.
- Task assignment for specialists to assign documents and annotation or review tasks to individual subject matter experts.
- Built-in OCR capabilities to detect scanned pdf files and convert them into machine-readable files capable of being annotated.
- Sophisticated table extraction to parse and extract information from tables regardless of the row/column format in the PDF document.
- Active assistance that includes inline messages pop up to guide users when creating new annotations.
Cortical.io Contract Intelligence is available immediately as a stand-alone application or can be integrated into a workflow through the use of REST APIs. The product can be delivered on-premises, in a private cloud or a public cloud. Cortical.io also is partnering with integration partners and other product solution vendors. The solution is licensed on an annual basis and includes maintenance and support.
https://www.cortical.io
Obie announced the launch of its new browser extension to democratize access to Obie’s core search, access, and knowledge sharing functionality. They also launched Personal Pro and Personal Free plans that decouple Obie’s search functionality from Slack for the first time to expand availability for individuals and remote teams. Obie uses natural language processing (NLP) to understand complex queries, as well as machine learning (ML) to improve results with every document search. Users can also manually add “FAQs” to store templates, text snippets, and frequently accessed information by simply highlighting the information in their browser and adding it to Obie.
https://obie.ai
Serviceaide, Inc., a provider of intelligent, enterprise service management solutions, announced the launch of Luma Knowledge, a self-learning, knowledge-centered product that optimizes access, creation and reuse of enterprise knowledge to service and support needs of users and customers. The maker of the AI-powered Luma Virtual Agent, Serviceaide is leveraging AI technologies like natural language processing and machine learning in digital interactions, knowledge and automation to bring advanced capabilities and business value to service and support functions across the enterprise. Features and capabilities:
- The Luma Knowledge hub provides a common tool to actively correlate and access information federated across the enterprise.
- Luma Knowledge offers a common semantic pathway to all enterprise knowledge.
- Natural Language Processing auto extracts topics and pulls text from complex documents to auto-create FAQs.
- A dynamic guided search capability, based on available knowledge, helps users access the right information even when they don’t know exactly what to ask for, and don’t know what is in the knowledge base.
- Automated learning leverages machine learning to auto-tune retrievals and identify missing content or other related issues.
- Knowledge Sharing – Federating across multiple knowledge bases, semantic searches and guiding requests deliver accurate knowledge
- Knowledge Discovery – Proactively discovering knowledge both inside an organization and from external sources
- Knowledge Improvement – Continuous monitoring of knowledge and feedback to provide recommendations for needed knowledge, correcting knowledge and searches, and retiring unused knowledge
https://serviceaide.com
A graph database uses graph structures with nodes, edges, and properties to represent and store data. By definition, a graph database is any storage system that provides index-free adjacency. This means that every element contains a direct pointer to its adjacent element and no index lookups are necessary. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
Ontotext (OT) and Semantic Web Company (SWC) announced a strategic partnership to meet the requirements of enterprise architects such as deployment, monitoring, resilience, and interoperability with other enterprise IT systems and security. Users will be able to work with a feature-rich toolset to manage a graph composed of billions of edges that is hosted in data centers around the world. The companies have implemented an integration of the PoolParty Semantic SuiteTM v.8 with the GraphDB and Ontotext Platform, which offers benefits for numerous use cases:
- GraphDB powering PoolParty: Most of the knowledge graph management tools out there bundle open-source solutions that are good at managing thousands of concepts, whereas PoolParty bundled with GraphDB manages millions of concepts and entities—without extra deployment overheads.
- PoolParty linked to high-availability GraphDB cluster: GraphDB can now be used as an external store for PoolParty, which offers a combination of performance, scalability and resilience. This is particularly relevant for organizations intent on developing tailor-made knowledge graph platforms integrated into their existing data and content management infrastructure.
- Dynamic text analysis using big knowledge graphs: PoolParty can be used to edit big knowledge graphs in order to tune the behavior of Ontotext’s text analysis pipelines, which employ vast amounts of domain knowledge to boost precision. This way the power and comprehensiveness of generic off-the-shelf natural language processing (NLP) pipelines can be custom-tailored to an enterprise.
- GraphQL benefits for PoolParty: Application developers can now access the knowledge graph via GraphQL to build end-user applications or integrate knowledge graph services with the functionality of existing systems. Ontotext Platform uses semantic business objects, defined by subject matter experts and business analysts, to generate GraphQL interfaces and transform them into SPARQL.
https://www.ontotext.com/, https://www.poolparty.biz
ProQuest is improving the accessibility of subscription and open access content on its platform with a series of enhancements designed to boost research, teaching and learning outcomes. These enhancements include:
- A new starting point for research: Now, users can begin their search from the open web by visiting search.proquest.com. Through their search results, they’ll be delivered straight to the resources their library subscribes to.
- New preview feature: Users can search, find and preview the content of nearly a billion ProQuest documents directly from the open web for better discoverability.
- Broader discovery of open access content: Researchers can access an ever-expanding universe of scholarly full-text open access sources directly – all indexed and delivered with the same level of quality and precision as ProQuest’s subscription content.
These enhancements are now live, with no action required by libraries or their users to activate. They’re part of ProQuest’s larger, ongoing initiative to add value to its solutions, expand pathways to access and help libraries increase usage of their resources.
https://www.proquest.com
