#nlp examples

Natural Language Processing (NLP) is the use of computers to generate and/or understand written and spoken languages for some practical purposes such as machine translation, natural language queries, question answering systems and so on. Natural language understanding is sometimes referred to as an artificial intelligence complete problem. That is, the difficulty of natural language understanding is equivalent to solving the artificial intelligence problem of making computers as intelligent as humans.

Natural language processing is the ability of computer programs to understand human speech as it is spoken. NLP is a component of artificial intelligence (AI). Current approaches to NLP are based on machine learning, a type of AI that examines and uses patterns in data to improve a program's understanding. Most of the research being done on NLP revolves around search, especially enterprise search. Common NLP tasks in software programs today include:

Sentence segmentation

Part of speech (POS) tagging

Sentence parsing

Deep analytics

Named entity extraction

Co-reference resolution

Natural language processing is a subfield of AI and linguistics. NLP is a theoretically motivated range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis for the purposes of achieving human-like language processing for a range of tasks or applications. Usually, there exist multiple methods or techniques to accomplish a task or language analysis. For instance, a POS tagging problem can be achieved using rule-based system, fuzzy systems, statistical systems or a hybrid system combining one or more of these.

A natural text can belong to any language, mode, genre etc. The text can be written or oral. The text should not be formatted in a such a way for the analysis task. Rather, the text should be what is known as "running text", and in actual usage. There are several levels of linguistic analysis in which a text can be analyzed: phonological (sounds), morphological (words), syntactic (sentences), semantic (words, sentences and meaning), pragmatic (what is beyond the surface meaning) and so on.

Computers are expected to process natural language texts or sounds in the same way as human do. As a matter of fact, it is too difficult to achieve a 100% processing similar to that of human beings. That is because natural language is highly ambiguous, and whatever artificial intelligence system is used is bound to commit mistakes. However, satisfying results have been achieved recently.