ABOUT NORCOM
Compliance-compliant classification of documents
What sounds banal at first glance poses problems for many AI users in practice: the heterogeneous, distributed data must be made available to the AI system in a form that can be evaluated. DaSense simply docks onto any existing system and imports all formats and types of files. Information in the files is made machine-readable and automatically labeled.
The task
The customer would like to automatically classify his group-wide documents into different categories according to a predefined system in order to achieve reliable data life-cycle management and compliance-compliant filing of the documents.
The challenge
The huge amount of data (petabyte scale). There are a large number of document types with very heterogeneous document contents.
our solution
A natural language classifier based on machine learning was implemented. In a first step, all necessary data was ingested, as well as its exploration, cleaning and classification according to file type and language. One focus was on German-language Word, PDF and e-mail documents, which make up a large part of the data to be analyzed. A high level of accuracy in the classification of the documents could then be achieved with little effort. The solution used proved to be decisive for the efficient creation and evaluation of the solution, in particular due to the big data native scalability and the flexibility and speed with which new algorithms can be provided in an enterprise environment.
The customer benefit
New documents are automatically tagged and classified so that reliable life cycle management can be mapped. In the course of increasingly strict regulations, an important function to ensure compliance at all levels and by all employees.
What sounds banal at first glance poses problems for many AI users in practice: the heterogeneous, distributed data must be made available to the AI system in a form that can be evaluated. Ingest-App reliably takes care of this.
Functions: Recording of all file types, creation date, authors, mdf ingest, preparation for full-text search, deduplication, multidimensional filing, information extraction
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Currently in use, e.g., in the measurement data analysis in the development department of an automobile manufacturer
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Someone keeps track!labelingscans documents from all angles and provides them with metadata. In this way, no information is lost and those who search will always find the right thing!
Features:Weak Learning & Machine Learning, Speech Recognition, Author Recognition, Classification, Named Entity Recognition
TheLanguage appis a language talent: It recognizes the language of a document based on the entire text and allows filtering according to this characteristic.
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Features:'Speech recognition, creating a filter for 'the language, confidence of recognition
Your advantages with DaSense
tested
Organization
- any order Dimensions
DaSense offers multidimensional storage structures, so-called facets, which can be combined and filtered as desired. There are also clear annotations for documents and clear versioning.
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Features:
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Property facets: Multidimensional filing structure based on document properties such as language, document type, etc.
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Workflow facets: Multidimensional storage structure according to processing status, evaluation, etc.
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Annotations: Linking properties to individual parts of the document, i.e. sentences, sections or images
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Advantages
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Supplementing the existing folder structure with practically relevant categories
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Illustration of complex relationships
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Linking multiple facets
Organization
- any order Dimensions
DaSense offers multidimensional storage structures, so-called facets, which can be combined and filtered as desired. There are also clear annotations for documents and clear versioning.
​
Features:
-
Property facets: Multidimensional filing structure based on document properties such as language, document type, etc.
​
-
Workflow facets: Multidimensional storage structure according to processing status, evaluation, etc.
-
Annotations: Linking properties to individual parts of the document, i.e. sentences, sections or images
​
Advantages
-
Supplementing the existing folder structure with practically relevant categories
-
Illustration of complex relationships
-
Linking multiple facets