Natural Language Processing is a part of Computer Science also known as Computational Lingusitics. NLP is creating representations of data and make the data structure more efficient for processing.
NLP is nothing but processing of the human language, whatever may be the language, mode or genre. Undersatnding the practicality of the data thats generated, and its about live human data, real time, not any fabricated data. Creating visualizations of the data is what helps in understanding and creating demands, products and services.
In NLP there are two goals that need to be achieved. First is collecting the data and storing and organizing the data in a way, so it can be retrieved easily whenever required.
Second is, mining and refining the data, and creating data visualizations and presentations which can be easily understood by humans. A comprehensive and readable visulaization is what is required by entrepreneurs and organizations to come up with creative ideas and make innovations. This is what makes the market and commerce grow and bring in improvements in ecosystems.
Morphology is one major part of NLP, which helps in creating the data visualizations. Morphology deals with the study of words, their formation, their structure and their meaning. For example, the word ‘refinement’ can be analyzed as three seperate morphemes, the prefix ‘pre’, the core word ‘fine’, and the suffix ‘ment’. An NLP system identifies the meaning cnveyed by each morpheme in order to represent the meaning. For example, adding the suffix ‘-ize’ to a verb, conveys that the action needs to be performed or has been asked to perform. Adding the suffix ‘-ed’ to a verb denotes that the action has been performed, something took place in the past. This part of NLP may seem to be clear but there are still difficulties in analysis of certain words.
If you would like to get into the details of Morphology, i’ll recommend Lecture 7 Morphology, its on University of Pennsylvania’s website under Introduction to Linguistics by Instructor Mark Liberman.
photo source: dullhunk
- Mining the blogosphere (eurekalert.org)
- Solving the NLP Challenge in Health Care (semanticweb.com)
- To parse natural language data, look at jStart NLP (ibm.com)
- Natural language processing fun (zemanta.com)