Whether you are involved in academic or business research the information you are mining is most likely housed in a database or on the web. Research processes for the most part involve analyzing and searching through large amounts of documents, publications and articles looking for specific data or information.

Text analysis and NLP has changed how we tackle research projects. Modern technology allows researchers to focus on more than just the presence of keywords in a text to decide on its relevancy. It allows researchers to focus on semantic aspects of text to get a lot more targeted in research methods.

It also allows you to automate a lot of the mundane tasks associated with research like summarization, classification and extraction to dramatically reduce research times.

Problem:

Conducting traditional research is a long, tedious and manual process.

Solution:

Text Analysis automates mundane research tasks and removes inefficiencies.

Is there a pattern?

research

Use Cases

Academic Research

  • Discover and identify articles and publications with more than keyword search.
  • Classify and categorize documents.
  • Automatically summarize documents into consumable chunks to reduce research time.
  • Extract entities from documents, articles and publications.
  • Extract authors and references automatically.

Business Research

  • Scan internal and external data sources with more than keyword search.
  • Extract entities from documents, articles and publications.
  • Extract relevant media from URL’s and articles like text, images, video.
  • Tag and classify documents automatically.
  • Summarize docs for quick and easy reference.

Features

The features listed below outline the API functions that are most commonly used in Research use cases.

Concept Extraction

Extract concepts from a text to allow enhanced search and discovery capabilities.

Summarization

Automatically summarize documents, articles, publications and URL’s into consumable chunks for quick reference.

Clustering

Automatically group documents together based on Semantic or Syntactic characteristics.

Language Detection

Automatically detect what language documents are written in.

Entity Extraction

Extract named entities mentioned in a document and cross-reference them to DB Pedia and schema.org.

Keyword Extraction

Automatically extract and list keywords from documents, articles and URL’s.