Box announced the addition of intelligent, automated classification to Box Shield, the company’s security solution for protecting content in the cloud. Leveraging machine learning, Shield can now automatically scan files and classify them based on their content, helping businesses detect and secure sensitive data without getting in the way of work. Box Shield helps prevent data leakage and proactively identifies potential insider threats or compromised accounts.
Using machine learning and data leakage prevention capabilities, this new feature scans files in real-time when they’re uploaded, updated, moved, or copied to specified folders, and automatically classifies them based on admin-defined policies. This enables customers to scale data classification and enforce policies across the enterprise, in order to reduce risk and meet compliance standards such as HIPAA, PCI DSS, and GDPR. Customers will be able to:
- Automatically identify multiple personally identifiable information types within files, including social security numbers, driver’s licenses, International Bank Account Number (IBAN) codes, International Classification of Diseases (ICD-9/ICD-10) codes, and more
- Automatically identify custom terms and phrases within files – for example: “Box Confidential”, “Internal use only”, and “NDA required”
- Easily create policies that apply the appropriate classification label based on desired logic – including and/or conditions and unique counts
Once files are classified appropriately, Shield can help prevent data leakage through a combination of access controls already in use by Shield customers, such as shared link, external collaboration, and download restrictions. The new feature supports the most common unstructured file types in Box, including documents, spreadsheets, PDF, Box Notes, and more. The new Box Shield automated classification capabilities will begin to be available today and will roll out to eligible customers over the next month.
Neofonie announced that TXTWerk – Text mining for SAP solutions, a framework application is now available for trial and online purchase on SAP App Center, the digital marketplace for SAP partner offerings. TXTWerk is delivered online as a subscription service and integrates with SAP and third-party software through the API management capabilities of SAP Cloud Platform Integration Suite. TXTWerk enables the extraction of metadata from texts, providing structured data from unstructured texts. By applying machine learning techniques in combination with rule-based approaches, TXTWerk can read and understand texts quickly. Whether 1,000 or 10 billion documents need to be processed, TXTWerk recognizes the most important keywords, people, places, organizations, events and key concepts and links them to sources such as knowledge graphs or internal company data. Also, part of the framework are artificial intelligence (AI) processes for classification in classes defined by the customer, a sentiment analysis of texts, phrase and role recognition as well as the automatic linking of entities according to specially defined relations. In addition to the AI processes, TXTWerk comes with a knowledge graph with over seven million entries.
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.
Acquia announced a partnership with content migration software and services company Xillio to power its Acquia Migrate Re-Platform solution (formerly CMS Migrate). Using this solution, organizations can move their website’s content and data from other content management systems to Drupal 9 faster. In addition to Acquia Migrate Re-Platform, Acquia has several other solutions that will support organizations’ migrations to Drupal 9, including Acquia Migrate Analyze, which allows customers to extract their content and data from any non-Drupal CMS into reports to investigate their data model and optimize their content before migrating to Drupal 9. This solution is also offered in partnership with Xillio.
Mercatus announced the availability of PDF Parser, technology-augmented PDF data extraction for private markets. The Mercatus platform’s PDF Parser feature mitigates the challenges of data on-boarding, help to eliminate manual extraction of asset reports, investor memos and other custom reporting. Because PDFs are designed for humans and not computers, they do not have a defined structure that allows users to gather data from it easily. The Mercatus data management platform allows users to query, search, filter, merge, sort and extract texts and images from any PDF documents in an effective way. Features include:
- Document Parser Templates – leverage configurable document Parser templates for automated and repeatable data extraction from assets, performance reports, investor memos and more.
- Batching and Historical Entry – Upload a batch of PDFs at one time to load data for single or multiple entities. Upload decades worth of data in minutes.
- Auditing and Governance – Construct data lineage across an entire investment portfolio. Track and audit where data is coming from, how it is being used and who is using it.
Expert System announced the release of the expert.ai NL API, a cloud-based Natural Language API that enables data scientists, computational linguists, knowledge engineers and developers to easily embed advanced Natural Language Understanding and Natural Language Processing capabilities (NLU / NLP) into their applications. The free expert.ai NL API provides natural language understanding capabilities based on Expert System’s symbolic approach that leverages AI-based algorithms, machine learning and knowledge graph to provide advanced features for reading and understanding any text, out of the box. Developers and data scientists can bring NLU applications such as text analytics, search and insight engines, content enrichment, tagging and processing, chatbots and virtual assistants, sentiment analysis, email management, contract exposure comparison etc. to market faster. expert.ai NL API features include:
- Deep linguistic analysis that parses each sentence into tokens, lemmas, parts of speech and phrases
- Accurate syntactic analysis that enables the extraction of entity relationships
- Word sense disambiguation that resolves semantic ambiguities by leveraging Expert System’s knowledge graph, pinpointing the precise meaning of concepts in context
- Precise named entity recognition and linking that identify people, companies, locations and other entity types, linking them to leading knowledge bases like Wikidata, Geonames and DBpedia
- Document classification based on IPTC Media Topics