What are Text Mining, Text Analytics, and Natural Language Processing?

 

Text mining, also known as text analytics, is an artificial intelligence technology (AI). It uses natural language processing to transform unstructured text from documents and databases into structured data that can be used for machine learning (ML) or analysis.

This section provides an overview of these technologies and highlights some features that make them effective. Below is a short video (90 seconds) on text mining and natural language processing.

What is Text Mining?

Text mining is a widely used tool in knowledge-driven companies. It involves the analysis of large documents to uncover new information or answer research questions.

Text mining is a way to identify facts, relationships, and assertions that are otherwise lost in text data collection. This information can then be converted into a structured format that can be further analyzed or presented directly with clustered HTML tables and mind maps, charts, etc. There are many methods to extract text from the web. The most important is Natural Language Processing (NLP).

Text mining creates structured data that can be used to integrate into data warehouses, databases, or dashboards for business intelligence.

What is Natural Language Processing?

Natural Language Understanding allows machines to "read" text or other inputs, simulating human comprehension of natural languages such as English, Spanish, and Chinese. Natural Language Processing can be described as Natural Language Understanding or Natural Language Generation. This simulates the human ability create natural language text, e.g. To summarize information or participate in a dialog.

Natural language processing is a technology that has advanced over the past ten year. Products such as Siri, Alexa, and Google's voice-search use NLP to understand user requests and respond accordingly. Advanced text mining tools have been created in many areas, including medical research, risk management, customer service, fraud detection, and contextual advertising.

Natural language processing systems today can easily analyze text-based data in an unlimited amount of time and with a consistent, impartial approach. They are able to understand complex contexts and decode ambiguities in language to extract key facts, relationships or provide summaries. This automation is essential to efficiently analyze text-based data, given the volume of unstructured data produced each day.

Machine Learning and Natural Language Processing

Machine learning (also known as artificial intelligence or AI) is a technology that allows systems to learn from their experience and solve complex problems with precision that rivals or sometimes exceeds humans.

Machine learning, however, requires well-curated input. This is not possible from electronic health records (EHRs), or scientific literature, where the majority of data is unstructured text.

Natural language processing, when applied to EHRs or clinical trial records, can extract structured data that is needed to drive advanced predictive models used for machine learning. This reduces the need to manually annotate training data.

This 15-minute presentation by David Milward, the CTO of Linguamatics discusses AI in general, AI technology such as natural language processing, machine learning, and how NLP can be combined to create new learning systems.

Natural Language Processing at Enterprise Level

Advanced analytics offers real potential in the healthcare and pharmaceutical industries. The challenge is to select the right solution and then implement it efficiently throughout the enterprise.

A number of features are required for effective natural language processing. These features should be included in any enterprise-level NLP solution.

Analytical Tools

Document composition and context are vastly different, with many sources, formats, grammar, and language. This variety can be tackled using a variety of methods:

Transform internal and external formats of documents (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image data collection) into a standardized searchable format;

Ability to search, tag and identify specific sections of a document.

Linguistic processing is used to identify meaning units in text, such as sentences, nouns and verb groups along with their relationships.

These tools use semantics to identify and normalize concepts in the text, such as drugs or diseases. Many organizations require the ability to create their own dictionaries in addition to core healthcare ontologies like MedDRA or Mesh.

Pattern recognition is used to identify and distinguish information that cannot be easily described using a dictionary approach. These include dates, numbers, biomedical terms, and other information (e.g. Concentration, volume, dosage, and energy;

Ability to process embedded tables in text, regardless of whether it is HTML, XML or free text.

Open Architecture

Open architecture, which allows the integration of various components, is now a key aspect of the development of enterprise system. There are several key standards in this area.

Integration with document processing workflows is possible through a RESTful Web Services API

A declarative query language that can be read by humans and is accessible for all NLP functionality (e.g. query, search terms, context, and display settings;

Ability to integrate and transform extracted data into a common infrastructure (MDM) for master data management (MDM), or distributed processing with e.g. Hadoop.

Technology Partners

Industry innovators need partnerships to gain access to the technologies and tools needed to transform data throughout the enterprise.

Linguamatics collaborates and partners with many companies, universities and government organizations to provide customers the best technology and next-generation solutions. For more information on our technology and content partnerships, visit our Partners and Affiliations Page.

Interface for the User

A user interface that is intuitive and easy to use allows for greater access to natural language processing tools. Programming expertise, command-line access, scripting.

NLP solutions that are productive offer a variety of access options to the platform. These can be used to meet the business requirements and skills sets across the organization.

A graphical user interface (GUI), which is intuitive and does not require users to create scripts.

Access to web portals for non-technical users

A search interface for browsing ontologies.

A web interface that allows users to access data and to process indexes on their behalf.

Domain experts can ask questions using a wide range of query modules that are not part of the standard set.

Scalability

Text-mining problems can be large, ranging from occasional access to few documents to federated search over multiple silos or millions of documents. The modern natural language processing solution must be:

Allow you to run complex queries on tens of thousands of documents. Each document could be thousands of pages in length.

Manage vocabularies, ontologies and other resources that contain millions of terms.

You can run parallel architectures, such as standard multi-core, cluster, or cloud.

Connector to enable natural language processing in service-oriented environments like ETL (Extract Transform, Load), signal detection and semantic enrichment.

What can GTS do to help?

Global Technology Solutions understands your needs for high-quality AI training data. Global Technology Solutions offers high-quality data tailored to your needs. Our team has the experience and expertise necessary to complete any task quickly. We are able to provide support in more than 200 languages and are ready to tackle any task. GTS provided image, text, video and ADAS data collection services.

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