Natural Language Processing, also known as NLP, concerns with the ability of devices to understand human communication. It is a subject of artificial intelligence and computer science. It has various applications in modern gadgets. The security devices, as well as google translate, even use NLP. Therefore, it has made different tasks easier in daily life. However, this technology still requires some improvement. In this article, we describe all the relevant details regarding natural language processing. The individuals must read this article very attentively until the last to get the proper knowledge.
Natural Language Processing Tasks
There are various activities performed by NLP. They are:
- Automatic summarization
- Named entity recognition (identify people, places, and organizations)
- Natural language generation (convert information into readable language)
- Co-reference resolution (determine which words refer to the same objects, especially for pronouns)
- Natural language understanding (convert chunks of text into more formal representations)
- Speech recognition (convert audio to text)
- Sentiment analysis (classify text as favorable or unfavorable toward specific objects)
- Part-of-speech tagging
How NLP Works?
The two main techniques involved in NLP are Semantic and syntax. The arrangement of words to make a grammatical sense in any sentence is syntax. Therefore, NLP uses it to assess the meaning of any language, depending on the rules of grammar. This technique involves:
- Parsing ( which perform grammatical analysis for a sentence)
- Sentence breaking (which places sentence boundaries in significant texts)
- Word segmentation (In other words, it divides a large piece of text to units)
- Stemming (which separates words with inflection in them to root forms)
- Morphological segmentation (In other words, it divide words into groups)
Another essential technique involved in NLP is Semantics. It comprises of meaning behind words and their use. The algorithms help NLP to understand the structure and also the purpose of sentences. The semantics include:
- Named entity recognition (which determines words of different categories into groups)
- Word sense disambiguation (In other words, it derives the meaning of a word based on context)
- Natural language generation (which use a database to determine semantics behind words)
Gensim, NLTK, and Intel NLP Architect are the three tools used in NLP. Document indexing, as well as topic modeling, are the uses of python library genesis. NTLK, also known as Natural Language Toolkit, is a python module that is open source and also contains tutorials as well as data sets. Another python library used for deep learning techniques, along with topologies, is Intel NLP Architect.
Benefits Of Natural Language Processing
NLP has multiple advantages. It includes:
- The ability to automatically make a readable summary text
- Improved accuracy as well as the efficiency of documentation
- Easier to perform sentiment analysis
- Allows an organization to use chatbots for customer support
- Useful for personal assistants such as Alexa
Challenges Associated With NLP
The NLP has still needed some modifications. One of the significant problems for this language processing process is semantic analysis. Moreover, it is difficult for programs to understand the general use of language. There are various pronunciations of the same letters in different words. The way of using the language by people is changing continually. It can also be a challenge to the NLP. Therefore, the programmers are making various changes to make the technology better.
In the above article, we have provided all the details about Natural Language Processing. People who are desperate to know about it then they must read this post till the last. Moreover, you can share your comments and queries in the below comment box.