Text Collection Techniques for Named Entity Recognition and Entity Linking

Text Collection (also called the use of text analytics) is an artificial intelligence (AI) technology that employs natural processing of language (NLP) to transform unstructured (unstructured) text found in databases and documents into structured, normalized data that is suitable for analysis, or to power the machine-learning (ML) algorithmic processes.

What is Text mining?

A lot of knowledge-driven companies use text mining Text mining refers to the procedure of studying massive collections of documents in order to uncover new information or answer specific research queries.

Text mining uncovers the truth, relationships and assertions that otherwise be lost in the big textual data. Once the data is extracted, it is transformed into a formatted form that can be further studied or displayed directly with clustered HTML tables chart, mind maps and more. Text mining utilizes a number of techniques to process text, the most significant includes Natural Language Processing (NLP).

Text mining produces structured data that text mining can be incorporated into data warehouses, databases as well as business intelligence dashboards, and can be used to perform descriptive, prescriptive , or predictive analytics.

What is Natural Language Processing (NLP)?

Natural Language Understanding helps machines "read" text (or another input, such as speech) by resembling the human capacity to read natural languages like English, Spanish or Chinese. Natural Language Processing includes both Natural Language Understanding and Natural Language Generation which mimics the human capacity to write natural language texts e.g. to summarise information or participate in a dialog.

As a technique that has been developed, 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 been created in areas such as medical research and risk management, as well as customer care and Insurance (fraud identification) and contextual ads.

The modern day systems for natural language processing are able to analyze endless amounts of text-based data with no fatigue, and in a uniform unbiased method. They can comprehend concepts within complex contexts, and understand the language's ambiguities to find the most important information and relationships or give summary reports. With the large amount of unstructured data produced each day from Electronic Health Records (EHRs) as well as social media postings, this kind of automation has become essential to analyzing text-based information efficiently.

Machine Learning and Natural Language Processing

The machine learning process is an artificial intelligence (AI) technology that gives systems that can automatically learn from experiences without the requirement for explicit programming and help solve difficult problems with precision that can be compared to or even surpass humans.

Machine learning, however, requires well-curated Data collection company to train from which is usually not accessible from sources like EHRs (EHRs) or the scientific literature, where the majority of information is text that is not structured.

If applied to EHRs, clinical trial records, or even full texts Natural language processing is able to collect the clean, organized information needed to power the advanced predictive models employed for machine learning. cutting down on the expense of manual annotation of the training data

In this presentation of 15 minutes, David Milward, CTO of Linguamatics talks about AI broadly, AI technologies such as natural language processing, as well as machine learning, as well as what NLP as well as machine-learning could be used together to create various learning systems.

Big Data and the Limitations of Keyword Search

While traditional search engines such as Google are now offering refinements like synonyms, auto-completion, and semantic searches (history and contextual) however, the majority of results provide the address of documents, leaving users with the burden of spending long hours manually extracting the required information by reading the individual documents.

The shortcomings of traditional search are further complicated by the exponential growth in big data in the last 10 years, and has enabled increase the number of outcomes that are returned from a search engine such as Google from the tens of thousands to hundreds of millions.

The biomedical and medical sectors of healthcare aren't any different. A study published in December 2018 from the International Data Corporation (IDC) discovered that the amount of data that is big is expected to grow more quickly in the healthcare sector than manufacturing media, financial services or manufacturing in the coming seven years. witnessing an annual compound rate of growth (CAGR) at 36 percent.

IDC White Paper: The Digitalization of the World from Edge through the Core.

As the volume of big data textual and the increasing use of AI technologies like natural machine learning and language processing is getting more important.

Ontologies, Vocabularies and Custom Dictionaries

Ontologies, vocabularies and customized dictionary are effective tools that assist in search, data extraction, and data integration. They are an essential element of many tools for text mining that provide an overview of the most important concepts with synonyms and names typically laid out in a hierarchical order.

Text analytics tools , and natural language processing software can be made even more effective when they are paired with domain-specific ontologies. Ontologies can allow the actual significance of the words to be achieved regardless of whether it is presented in various ways (e.g. Tylenol or. Acetaminophen). NLP techniques enhance the effectiveness of ontologies, such as by allowing matches of terms that have different spellings (Estrogen as well as Oestrogen) as well as by taking into account context ("SCT" could refer to the gene "Secretin", or to "Stair Climbing Test").

The definition of an ontology consists of the vocabulary of terms used and formal restrictions on 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 commonly used concepts;
• Methods based on patterns for categories like chemical, measurements, and mutations. names that may include new (unseen) concepts;
• Concept identification that is specific to the domain, based on rules Transformation, annotation and identification;
• Integration of customer vocabularies in order to allow a custom annotation
• Advanced search that allows for an identification process of ranges in data that include dates numeric values, areas concentration, percentage, length, and duration.

Linguamatics provides a range of ontologies, standard terminologies and vocabularies to complement its natural processing platform for language. More details can be found in the Ontologies page.

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 various elements that must be integrated in any business-level NLP solution. A few of them are listed in the following

Analytical Tools

There's a lot of variation in the composition of documents and their textual context, which includes formats, sources, and grammar. To deal with this diversity, you must employ different methods of analysis:

• Transformation of external and internal formatted documents (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format;
• The ability to locate tags and search within particular documents sections (areas) For example using a focus search to block out the noise of a document's reference section
• Processing of language to find the important units of text , like sentences, nouns and verb groups, as well as the connections between them.
• Semantic tools that help identify the meaning of concepts within texts like drugs and diseases and standardize concepts that are standard ontologies. In addition to the essential ontologies for life science and healthcare like MedDRA and MeSH the capability to create their own dictionaries is an essential requirement for numerous organizations.
• Pattern recognition is used to identify and discover categories of information which are difficult to define using the use of a dictionary. This can include dates, numerical data and biomedical terminology (e.g. volume, concentration dose and energy) as well as gene/protein mutations.
• The capability to embed tables into the text, regardless of whether they are they are formatted with HTML or XML or in free text.

Open Architecture

A flexible architecture that allows for the integration of diverse components is now an essential element in the creation of enterprise systems. there are several essential standards in this space that are

• An RESTful Web Services API supports the integration of document processing workflows;
• The language of a declarative query that's understandable and accessible to anyone who needs NLP functions (e.g. search terms, queries display settings, context and display settings);
• The capability to transform and integrate the extracted data into a common platform to manage master data (MDM) and distributed processing using e.g. Hadoop.

Technology Partners

Partnerships are an essential enabler for innovators in the industry to gain access the tools and technology needed to improve the quality of data throughout the business.

Linguamatics works with and partners with a variety of companies, universities and governmental institutions to offer clients the appropriate technology for their needs , and also to create innovative solutions. Check out the Partners and Affiliations page for details about our content and technology collaborations.

User Interface

A well-designed user interface allows access to the tools of natural language processing and does not require specialist expertise to use these tools (e.g. programming skills commands line access, scripting).

A successful NLP solution can provide a range of methods to connect to the platform to satisfy demands of business and the different skill sets throughout the company for example:

• A simple GUI (GUI) that removes the need to write scripts
• Web portals which allow access to non-technical users
• A search interface that lets you browse ontologies
• A user interface for administration to manage access to data and permit indexes to be processed for the benefit of many users.
• A wide range of standard query options, which allows experts in the field to make inquiries without having to know the fundamental linguistics.

Scalability

Text mining challenges are diverse in their scope in scope, from a single access point to a handful of documents to multi-silos federated search with millions of records. Modern natural language processing system will, therefore, need to:

1. Give the capability to perform sophisticated queries on thousands of documents, which could have thousands of pages;
2. Utilize vocabularies, ontologies and vocabularies that millions of words;
3. Use parallel architectures, whether traditional multi-core cloud, cluster or
4. Connector to use natural language processing within service-oriented environments like ETL (Extract Transform), ETL (Extract Load), the enhancement of semantics and the detection of signals such as health care risk monitoring.

More Information

For more information about choosing the best tools for your needs in business you can read our article to selecting the best NLP solution for your business.

How GTS.AI can be a right Text Collection

GTS.AI can be a right text collection because it contains a vast and diverse range of text  data that can be used for various naturals language processing tasks,including machine learning ,text classification,sentiment analysis,topic modeling ,Image data collection and many others. It provides a large amount of text data in multiple languages,including English,spanish,french,german,italian,portuguese,dutch, russian,chinese,and many others.In conclusion, the importance of quality data in text collection for machine learning cannot be overstated. It is essential for building accurate, reliable, and robust natural language processing models.