What exactly is Text Mining, Text Analytics and Natural Language Processing?


Glance

Text mining (also called "text analytics) is an artificial intelligence (AI) technology that employs NLP (NLP) to transform unstructured (unstructured) text contained in databases and documents into structured, normalized data that can be used for analysis or driving machines learning (ML) algorithmic processes.

What exactly is Text Mining?

In many knowledge-driven businesses text data collection or text mining refers to the procedure of studying huge volumes of documents to find new information or to answer specific research queries.

Text mining uncovers the truth, relationships and assertions that otherwise get lost in the big data that is textual. After being extracted, the information is transformed into a formatted form which can then be further analyzed or presented in a direct manner using clustered HTML tables chart, mind maps and more. Text mining uses a number of techniques to process text, the most crucial includes Natural Language Processing (NLP).

The structured data generated by text mining can be incorporated into data warehouses, databases as well as business intelligence dashboards. They can be can be used to perform predictive, descriptive or prescriptive analytics.

What exactly is Natural Language Processing (NLP)?

Natural Language Understanding helps machines "read" text (or other inputs, like speech) by resembling the human ability to comprehend the meaning of a naturally spoken language, such as English, Spanish or Chinese. Natural Language Processing includes both Natural Language Understanding and Natural Language Generation which mimics the human capacity to create natural language texts e.g. to summaries information or engage in a dialog.

As a method of technology natural processing of languages has come of maturation over the past 10 years, with apps like Siri, Alexa and Google's voice search using NLP to recognize and respond to requests from users. Text mining tools that are sophisticated have also been developed for fields such as medical research as well as risk management, customer care and Insurance (fraud identification) and contextual ads.

The modern day systems for natural language processing are able to analyze endless quantities of text-based information without fatigue and in a constant and impartial method. They can comprehend concepts within complex contexts, and understand the language's ambiguities to find important facts and connections, or even provide summary information. Due to the massive amount of unstructured information that is produced each day including Electronic Health Records (EHRs) and social media updates, this type of automation is essential to analyzing text-based information efficiently.

Machine Learning and Natural Language Processing

Machine learning refers to an artificial Intelligence (AI) technology that gives machines with the capability to automatically learn from past experiences, without the requirement for explicit programming and help solve difficult problems with precision that can beat or even exceed humans.

Machine learning, however, requires carefully curated inputs to learn from which is usually not accessible from sources like Electronic Health Records (EHRs) or the scientific literature, where the majority of information is in unstructured text.

If applied to EHRs, clinical trial records, or texts natural language processing can provide the clean, structured information needed to power the advanced predictive models utilized to train machine-learning models, thus cutting down on the expense of manual annotation of the training data.

Big Data and the Limitations of Keyword Search

Although traditional search engines such as Google are now offering refinements like auto-completion, synonyms and semantic searches (history as well as context) However, the majority of results indicate the location of the documents, leaving searchers with the burden of spending long hours manually extracting the required information from each document.

The shortcomings of traditional search are further exacerbated by the increase in big data over the last 10 years, and has enabled increase the number of outcomes given for a single query through a search engine like Google from the tens of thousands to hundreds of millions.

The biomedical and medical sectors of healthcare aren't any different. A study conducted in December 2018 conducted by the International Data Corporation (IDC) revealed that the amount of data that is big is expected to increase faster in healthcare than manufacturing, media or financial services in the coming seven years. It is expected to experience an annual compound increase (CAGR) at 36 percent.

Vocabularies, Ontologies, and Custom Dictionaries

Ontologies, vocabularies and customized dictionary are effective tools that aid in data extraction, and data integration. They form a major component of a variety of text mining software that provide lists of important concepts with synonyms and names typically placed in a hierarchy.

Text analytics, search engines instruments and natural language processing software are even more effective when they're paired with specific ontologies for the domain in which they. Ontologies can allow the actual significance of the words to be comprehended regardless of whether it is written in various ways (e.g. Tylenol or. Acetaminophen). NLP techniques enhance the effectiveness of ontologies for instance by allowing matches of terms that have different spellings (Estrogen and Oestrogen) and also by taking into account context ("SCT" can mean the gene "Secretin" or "Stair Climbing Test").

The definition of an ontology consists of the definition of terms as well as strict rules for its usage. Natural language processing that is enterprise-ready requires a the use of multiple vocabularies, ontologies and other strategies for identifying concepts that are in the right context:

  • Thesauri, taxonomies, vocabularies and ontologies of concepts using well-known concepts;
  • Pattern-based methods for categorizing categories like measurements, mutations , and chemical names that may contain new (unseen) words;
  • Domain-specific, rule-based concepts identification Transformation, annotation, and domain-specific
  • Integration of customer-specific vocabularies to create custom annotations;
  • Advanced search options to allow to identify data-related ranges, dates numbers, areas concentration, percentage length, duration and weight.
  • Enterprise-level Natural Language Processing

Advanced analytics is a huge chance for healthcare and pharmaceutical industries where the difficulty is determining the right solution and then using it in a way that is efficient across the entire enterprise. A successful natural language processing demands several aspects that should be included in any business-level NLP solution. Some of these are discussed in the following.

1. Analytical Tools

There's a lot of variation in document composition and context, which includes formats, sources, and grammar. To deal with this diversity, you must employ various methods:

  • Transformation of both external and internal formatted documents (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format;
  • The capability to recognize tags and search within certain documents sections (areas) For example the ability to narrow a search in order to block out any noise in a paper's reference section;
  • Linguistic processing is used to determine the relevant units in text , like sentences, verbs, and noun groups, as well as the connections between them.
  • Semantic tools to identify concepts in the text, like diseases and drugs and then normalize them to concepts that are standard ontologies. Alongside the core medical and health ontologies, such as MedDRA and MeSH The ability to build their own dictionary is required by numerous organizations.
  • Pattern recognition is used to identify and determine categories of information which are difficult to define using the dictionary method. This includes dates, numerical data as well as biomedical terminology (e.g. volume, concentration, dosage energy) and mutations in genes or proteins;
  • The capability to process embedded tables in the text, regardless of whether they are it is formatted using HTML or XML or in free text.

2. Open Architecture

A flexible architecture that allows the integration of various components is an increasingly important component in the design of enterprise systems. there are many essential standards in this space for:

  • An RESTful Web Services API allows integration with workflows for document processing;
  • The language of a declarative query that's capable of reading and is accessible to all NLP functions (e.g. search terms, queries context, display settings and settings for display);
  • The capability to transform and incorporate extracted data into a shared infrastructure to manage master data (MDM) and distributed processing using e.g. Hadoop.

3. User Interface

A user-friendly interface that is effective can increase access to the tools of natural language processing instead of requiring special skills to utilize these tools (e.g. the ability to program commands line access, scripting).

A reliable NLP solution offers a variety of methods to connect to the platform to satisfy requirements of the business and to enhance the skills within the organization for example:

  • A user-friendly graphic user interface (GUI) that removes the requirement for users to write scripts
  • Website portals that allow access for non-technical users
  • A search interface that lets you browse ontologies
  • A user interface for administration to manage access to data and allow indexes to process by a variety of users.
  • A wide range of standard query options, allowing experts in the field to pose questions without having to know the language behind.

4. Scalability

Text mining challenges can range in size in scope, from simple access to the smallest of documents, to federated search across many databases and millions of files. Modern natural language processing software should therefore:

  • Give the capability to conduct sophisticated queries over hundreds of millions of documents, some of that could  contain thousands of pages;
  • Use vocabularies and ontologies that contain millions of terms.
  • It runs on parallel architectures whether they are standard multi-core cloud, cluster, or traditional multi-core
  • Connector to use natural language processing within environments that are service-oriented, such as ETL (Extract Transform, Load), the enhancement of semantics and the detection of signals such as the monitoring of risk in healthcare.

Conclusion

At Global Technology Solutions (GTS) we provide Quality Datasets for machines and applications to develop to this point they need to consume humongous quantities of text data. Our text data collection service includes multilingual texts some of them are

  • Chinese text dataset services
  • Dutch text dataset services
  • French text dataset services
  • German text dataset services
  • Italian text dataset services
  • Japanese text dataset services
  • Portuguese text dataset services
  • Spanish text dataset services

We have a vast text data collection that cuts across document dataset, receipt dataset, ticket dataset, business card dataset…etc.

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