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Information Extraction - The key to Question Answering Systems

The day AI reads a document and answers each and every question asked and do reasoning on it, will be the day when we will call it true intelligence. Welcome to the world of Information Extraction, where algorithms try to extract information from unstructured documents into structured information, which the AI can further access to answer questions. Apparently easy for humans perform such an important task, looks hard for AI to do.

The difficulty lies in recognizing named entities, identifying context, relationship extraction, understanding tables and diagrams, and many more. The research in Information Extraction has progressed exponentially since this problem was identified, and today we have lot of open source tools at our disposal.

Any toolkit for Information Extraction is expected to contain the following modules
  • Tokenizer - Converts a sequence of characters into a sequence of tokens
  • Gazetteers - Entity dictionaries used as a lookup table
  • Sentence splitter - Understands where a sentence begins and ends
  • Part of Speech (POS) tagger - Identify and tags part of speech in a text
  • Named Entity Recognition - Identify named entities in a text
  • Coreference Resolution - Identify multiple expressions that refer to the same entity

The following list shows some of the popular open source Information Extraction toolkits that contain most of the above modules

  • General Architecture for Text Engineering (GATE):
    GATE is a suite of tools developed in Java for Natural Language Processing tasks that includes ANNIE (A Nearly-New Information Extraction System) which contains all the basic modules for information extraction. 
      Check out GATE at https://gate.ac.uk/

  • Unstructured Information Management Architecture (UIMA):
    Unstructured Information Management applications are software systems analyze large volumes of unstructured information to discover relevant knowledge. IBM’s specialized Artificial Intelligence Watson that won the Jeopardy challenge uses UIMA. 
    Lean more about UIMA at https://uima.apache.org/

  • OpenNLP:
The Apache OpenNLP library is a toolkit for the processing of natural language text and supports all modules required for Information Extraction. OpenNLP also offers maximum entropy and perceptron based machine learning. 

  • Natural Language Toolkit (NLTK):
   NLTK offers NLP libraries in python to perform Information Extraction. It provides a strong integration with WordNet (lexical database for English language). 
   Explore NLTK at http://www.nltk.org/

With such vast array of open source Information Extraction toolkits you can create your custom Information Extraction software in few days. Almost 80% of business information is unstructured. Solutions that make that information structured by capitalizing on the above mentioned toolkits have huge value. The future is going to be on question-answer style querying the unstructured documents and not keyword based search.
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