Industrial Natural Language Processing & Information Extraction
Industrial Natural Language Processing
Industrial Natural Language Processing Overview Natural Language Processing Developing and applying techniques NLP and methods for the automatic POS processing of text tagging NLU keyword extraction summarization natural Industrial Natural Language topic language syntactic Processing recognition inference semantic parsing named parsing Developing and applying techniques entity and methods for the automatic recognition dialogue question sentiment agents processing of text in industry by answering analysis text explicitly considering the categorization requirements and circumstances of machine industrial environments translation Industrial Natural Language Processing & Information Extraction 3 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing Research Goals Reliable application and deployment of NLP in industrial environments Anonymization of textual data in order to be able to forward it to third parties Exploration of new areas for the use of natural language processing in the wild Industrial Natural Language Processing & Information Extraction 4 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing NLP in Industrial Environments Reliable application and deployment of NLP in industrial environments Software Architectures Usability Analyze ▪ Design, develop and evaluate software architectures to make NLP useable by non-technical users ▪ Improve the process of deploying and using NLP in industrial environments ▪ Analyze textual data based on state-of-the-art NLP approaches Industrial Natural Language Processing & Information Extraction 5 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing Anonymization Anonymization of textual data in order to be able to forward it to third parties ▪ The analysis of unstructured company data in the cloud is either undesired by the company itself or even forbidden by law (DSGVO) ▪ Cloud services provide more accurate and sophisticated analytics ▪ Develop new anonymization approaches by using machine learning as the current approaches are too inaccurate Industrial Natural Language Processing & Information Extraction 6 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing Exploiting new Application Domains Exploration of new areas for the use of natural language processing in the wild Identify Data Sources Derive Use Cases Explore possible Solutions ▪ Most of the data that is available comprises unstructured data and especially textual documents ▪ Identify available data sources and derive meaningful use cases from it ▪ Develop appropriate models and applications for the identified use cases that generate an additional value Industrial Natural Language Processing & Information Extraction 7 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Selected Research Projects and Applications
Industrial Natural Language Processing Integrating External Data into an Enterprise Information System Goal External Information Extraction Tool ▪ Integrating internal & external data into an enterprise information system to gain faster insights into changing markets, relations etc. Approach ▪ Identifying relevant data sources (e.g., new websites, social media, internal enterprise data) ▪ Integrating data into a common data storage News Websites Social Media ▪ Creation of d edicated analytical services for specific user requirements , like ▪ Natural Language Processing ▪ Translations Data ▪ Overview of business knowledge graph ▪ Sentiment analysis ▪ Recommendation of relevant data Enterprise Information System Results Information ▪ Personalized quick and easy access to a large amount of data from several different sources within a single tool Industrial Natural Language Processing & Information Extraction 9 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing Utilizing Textual Maintenance Data from Production Goal Maintenance Data Insights Tool ▪ Utilizing unstructured textual information from machines’ maintenance protocols to gain insights and optimize processes „… defect, please check“ „… part was exchanged “ Approach ▪ Extracting textual reports from maintenance staff ▪ Classify text into description of symptoms, causes and solutions „… machine losing oil“ „… spare part ordered “ ▪ Calculation of relevant statistics ▪ Creation of dedicated analytical services for staff and decision makers , like ▪ Occurrence of similar error descriptions over time and location ▪ Costs per machine location ▪ Maintenance Data Platform Troubleshooting proposal for specified symptoms Results Solution Hints Information ▪ Tool for ▪ assisted generation of maintenance report texts ▪ supported finding of solutions ▪ visualization of errors and costs Industrial Natural Language Processing & Information Extraction 10 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing Anonymization of Enterprise Documents using the Cloud Goal Hybrid Anonymizer ▪ Enable the usage of cloud services for data processing and analysis without revealing sensitive information Approach ▪ Development of an anonymization approach based on predefined rules and deep learning ▪ Implementation and testing of the hybrid anonymizer ▪ Deployment of the anonymizer within the customers ecosystem ▪ Methods: Natural Language Processing, Deep Learning, Micro-Service Architecture Results ▪ Functional hybrid system for the automatic anonymization/pseudonymization of textual data ▪ Enabled the use of cloud analysis for textual documents Industrial Natural Language Processing & Information Extraction 11 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing AISLE – Support learning academic phrases Goal AISLE ▪ Support students at the beginning of their studies in reading and understanding scientific publications Enter Word: Approach Interact ▪ Construct a large domain and target group specific text corpus using NLP methods ▪ Use recent methods in the area of natural language processing for extracting and evaluating words and phrases based on their relevance ▪ Development of an adaptive learning system to improve vocabulary on the basis of a developed learning algorithm Vocabulary Size and the built up corpora View Evaluate & Results Select Words Results ▪ Web platform that is actively used by students to improve their vocabulary ▪ User studies showed the system's positive impact on vocabulary growth Analyze Results Industrial Natural Language Processing & Information Extraction 12 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Information Extraction
Information Extraction Overview Information Extraction Unstructured “… Application of methods from practical computer science, Data POS artificial intelligence and computational linguistics to the tagging problem of automatic machine processing of unstructured information … ” Data-Specific Source: Wikipedia Processing Different Types of Structured named Unstructured Data entity Data recognition Data Analysis Structured Data named Results entity recognition Industrial Natural Language Processing & Information Extraction 14 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Information Extraction Research Goals Transformation of unstructured data into useful structured information and knowledge Leveraging of machine learning techniques to improve information extraction Industrial Natural Language Processing & Information Extraction 15 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Information Extraction Structuring unstructured data Transformation of unstructured data into useful structured information and knowledge Develop Processing Identify Information Choose Approaches Pipeline ▪ Identify all the relevant information that need to be extracted ▪ Identify approaches for extracting information from unstructured data and turning it into valuable knowledge ▪ Develop processing pipelines to automatically extract the identified information and make them accessible in a structured way Industrial Natural Language Processing & Information Extraction 16 Chair of Technologies and Management of Digital Transformation, University of Wuppertal
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