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NLP is the artificial intelligence term that gives computers the ability to learn about text or spoken words like humans do. NLP will be using computational linguistics and different types of rules-based modelling like statistical, machine learning and deep learning models. All these technologies will allow systems to process human language in the text form of voice data to learn the full meaning. NLP will have computer programs which will be translating the text that is in one language to another language and it also responds to the commands and would summarize the text briskly and in real-time. You can also easily interact with NLP with the help of voice-generated GPS systems, digital assistants, speech-to-text software, chatbots and customer conveniences.
NLP plays a critical role in enterprise solutions and would streamline operations, boosts the productivity of employees and would simplify business processes.
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It is also known as parsing where the machine would be identifying the syntactic structure that is in the text and the relationship between words. These are represented in the diagrammatic form known as a parse tree.
You can focus to identify the language's meaning. With the language being ambiguous, semantics is considered to be the most challenging part of NLP. Semantic would thoroughly analyze the structure of a sentence, interact with the words and concepts to find out the word's meaning and learn about the text in the topic.
It is a key task in natural language processing where the words would be broken into the semantical units known as tokens. The sentence tokenization would be split into sentences in the text and the tokenization would also be dividing words in a specific sentence.
Part of speech tagging
Parts of speech tagging would add the part of speech category to the token that is in a text. The best tags available for parts of speech are nouns, pronouns, conjunction, preposition, intersection and so on. The customer service in the sentence would be the noun, could well be the verb, better will be the adjective and I will be the punctuation.
Lemmatization and stemming
The word in the dictionary would be called the lemma. The terms such as were, am, is and been would be put together.
Stop word removal
The most essential step in the NLP process is to remove the stop word where the filtering of words would be happening at high frequency with little or no semantic value. The stop words could be which, too, at, for and so on.
Named entity recognition
It is the most popular task that is carried out in semantic analysis and would have extracted entities in the text. The entities that you can extract from the text can be names, places, email addresses and organizations.
Classification of text
The classification would learn about the meaning of the text which is unstructured and would be organized into pre-defined tags.
It will try to extract the subjective qualities, which can be emotions, confusion, suspicion and sarcasm.
Some of the popular topics in Natural Language Processing on which our programming assignment experts work on a daily basis are listed below:
|Stemming and Lemmatization using Bigrams
|Time optimization algorithm
|Finding unusual words
|part of speech and meaning
|Speech and sequence models
|Classify document into categories
|Sentiment Analysis with NLTK
|Work with Twitter streaming
|Word sense disambiguation
|Hidden Markov Models
|Application of Natural Language
The key functions that are performed by natural language processing algorithms include:
It will assign tags to the text that would be listed into categories. It is the helpful sentimental analysis that will allow natural language processing to learn the sentiment or emotion hidden in the text. The best example would be the reviews if brand A has a series of reviews, then the algorithm will find out how many of them are positive and how many of them are negative. It is best to be used for detecting the intent.
You can summarize the text automatically and find critical data pieces. One such example is keyword extraction which will get you a list of words from the text which are helpful for optimizing the search engine. For this natural language processing, you would need some programming skills. There are a lot of keyword extraction tools available to automate this process. It is that the user must set the parameters in the program. The tool will help you to extract the words that are often used in the text.
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