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Information extraction

What is information extraction?

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most cases, this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. 

Gathering detailed structured data from texts, information extraction enables:

  • The automation of tasks such as smart content classification, integrated search, management and delivery
  • Data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc.

How Does Information Extraction Work?

Given the capricious nature of text data that changes depending on the author or the context, Information Extraction seems like a daunting task. But it doesn’t have to be that way!

We all know that sentences are made up of words belonging to different Parts of Speech (POS). There are eight different POS in the English language: noun, pronoun, verb, adjective, adverb, preposition, conjunction, and intersection.

The POS determines how a specific word functions in meaning in a given sentence. For example, take the word “right.” In the sentence, “The boy was awarded chocolate for giving the right answer,” “right” is used as an adjective. Whereas, in the sentence, “You have the right to say whatever you want,” “right” is treated as a noun.

This goes to show that the POS tag of a word carries a lot of significance when it comes to understanding the meaning of a sentence. And we can leverage it to extract meaningful information from our text.

Typically, for structured information to be extracted from unstructured texts, the following main subtasks are involved:

  • Pre-processing of the text – this is where the text is prepared for processing with the help of computational linguistics tools such as tokenization, sentence splitting, morphological analysis, etc.
  • Finding and classifying concepts – this is where mentions of people, things, locations, events, and other pre-specified types of concepts are detected and classified.
  • Connecting the concepts – this is the task of identifying relationships between the extracted concepts.
  • Unifying – this subtask is about presenting the extracted data into a standard form.
  • Getting rid of the noise – this subtask involves eliminating duplicate data.
  • Enriching your knowledge base – this is where the extracted knowledge is ingested in your database for further use.

Information extraction can be entirely automated or performed with the help of human input.

Typically, the best information extraction solutions are a combination of automated methods and human processing.

Application of information extraction

Information extraction can be applied to a wide range of textual sources: from emails and Web pages to reports, presentations, legal documents and scientific papers. The technology successfully solves challenges related to content management and knowledge discovery in the areas of:

  • Business intelligence: For enabling analysts to gather structured information from multiple sources
  • Financial investigation: For analysis and discovery of hidden relationships
  • Scientific research: For automated references discovery or relevant papers suggestion
  • Media monitoring: For mentions of companies, brands, people
  • Healthcare records management: For structuring and summarizing patients records
  • Pharma research: For drug discovery, adverse effects discovery, and clinical trials automated analysis
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Information extraction

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is information extraction?

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most cases, this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. 

Gathering detailed structured data from texts, information extraction enables:

  • The automation of tasks such as smart content classification, integrated search, management and delivery
  • Data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc.

How Does Information Extraction Work?

Given the capricious nature of text data that changes depending on the author or the context, Information Extraction seems like a daunting task. But it doesn’t have to be that way!

We all know that sentences are made up of words belonging to different Parts of Speech (POS). There are eight different POS in the English language: noun, pronoun, verb, adjective, adverb, preposition, conjunction, and intersection.

The POS determines how a specific word functions in meaning in a given sentence. For example, take the word “right.” In the sentence, “The boy was awarded chocolate for giving the right answer,” “right” is used as an adjective. Whereas, in the sentence, “You have the right to say whatever you want,” “right” is treated as a noun.

This goes to show that the POS tag of a word carries a lot of significance when it comes to understanding the meaning of a sentence. And we can leverage it to extract meaningful information from our text.

Typically, for structured information to be extracted from unstructured texts, the following main subtasks are involved:

  • Pre-processing of the text – this is where the text is prepared for processing with the help of computational linguistics tools such as tokenization, sentence splitting, morphological analysis, etc.
  • Finding and classifying concepts – this is where mentions of people, things, locations, events, and other pre-specified types of concepts are detected and classified.
  • Connecting the concepts – this is the task of identifying relationships between the extracted concepts.
  • Unifying – this subtask is about presenting the extracted data into a standard form.
  • Getting rid of the noise – this subtask involves eliminating duplicate data.
  • Enriching your knowledge base – this is where the extracted knowledge is ingested in your database for further use.

Information extraction can be entirely automated or performed with the help of human input.

Typically, the best information extraction solutions are a combination of automated methods and human processing.

Application of information extraction

Information extraction can be applied to a wide range of textual sources: from emails and Web pages to reports, presentations, legal documents and scientific papers. The technology successfully solves challenges related to content management and knowledge discovery in the areas of:

  • Business intelligence: For enabling analysts to gather structured information from multiple sources
  • Financial investigation: For analysis and discovery of hidden relationships
  • Scientific research: For automated references discovery or relevant papers suggestion
  • Media monitoring: For mentions of companies, brands, people
  • Healthcare records management: For structuring and summarizing patients records
  • Pharma research: For drug discovery, adverse effects discovery, and clinical trials automated analysis
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