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Why natural language processing is important
By “natural language” we mean a language that is used for communication by humans such as English, German, or Korean. In contrast to artificial languages, the natural languages are hard to define with an explicit set of rules. By combining the power of artificial intelligence, computational linguistics and computer science, Natural Language Processing (NLP) helps machines to understand the natural language. The ability of a computer program to understand human language as it is spoken is Natural language processing.
A computer could be considered intelligent if it could carry on a conversation with a human being without the human realizing they were talking to a machine. -Alan Turing
The development of NLP applications is challenging because computers traditionally work with precise, unambiguous and highly structured languages such as programming languages, however, the natural language is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.
With the ongoing growth of the World Wide Web and social media, there is a drastic increase in online data. As the amount of data increases the mechanisms to process these unstructured data and to extract meaningful information from it becomes more challenging. It will be very difficult to find a specific piece of information from a large knowledge base. These challenges and difficulties can be overcome with the advanced NLP techniques.
The most widely used NLP application is machine translation which helps to overcome the language barriers. As the amount of information available online is increasing day by day, the need to access and process it becomes more important. To convert information from one language to another, machine translation can be used. The NLP techniques help the machine to understand the meaning of sentences, which improves the efficiency of machine translation.
The NLP techniques are very useful for sentiment analysis. It helps to identify the sentiment among several online posts and comments.The business firms make use of NLP techniques to know about the customer’s opinion about their product and services from the online reviews.
Using NLP the Automatic summarization can be performed more efficiently.Automatic summarization is relevant not only for summarizing the meaning of documents and information but also for understanding the emotional meanings of the information, such as in collecting data from social media. Automatic summarization is especially relevant when used to provide an overview of a news item or blog posts while avoiding redundancy from multiple sources and maximizing the diversity of content obtained. Using this the difficulty to find an important piece of information from a huge knowledge base can be reduced.
The advanced NLP techniques allow the non-programmers to interact with the computing systems and obtain useful information from it. Using NLP the common synonyms for the input phrases can be detected and match them with the right answers, it helps the users who are unfamiliar with the terminologies of the computing system. Spam filtering, language understanding, text classification, information extraction, question answering, Social website feeds, Voice recognition and speech-to-text are the other typical applications of NLP.
There are many open source Natural Language Processing (NLP) libraries and these are some of them:
- Natural language toolkit (NLTK)
- Gate NLP library
- Apache OpenNLP.
- Stanford NLP suite
NLTK is more popular and the leading platform for building NLP applications, which is written in python. It provides an intuitive framework along with substantial building blocks, consistent interfaces and data structures.