Natural language processing is a subpart of advanced computer science engineering and artificial intelligence that execute communications between computers/machines and human’s natural languages. With the help of NLP, smart machines and devices can easily sense about structured & unstructured form of data that can be helpful to take out valuable insights from it. Natural language processing as a part of artificial intelligence focuses on the approach of computing human languages to make intelligent machines and capable of understanding and analyzing human speech & voices. For example, you have noticed that your emails of your gmail account are automatically differentiate based on its category type and then saves into primary, social, promotion and spam folders that is only possible via NLP, performs on the methodology of text classification.
Importance Of Natural Language Processing (NLP)
Today as we know that there are millions of web searches generate by audience resulting in massive amount of data mounting everyday & it is required to maintain this massive data in a predefined way for smart machines to analyze and then make sense of it. Natural language processing system plays very imperative role in structuring of online data as it creates text and voice based speeches for machines so that it they can process the right information for users. NLP is used to carry out large scale analysis i.e. it can help machines to perform functionality concerned with language based tasks like reading & understanding the meaning of text information, sensing the prioritization of tasks, hearing speech or voice of online/offline users, extracting the useful insights from users sentimental views. For example if you want to predict the sentimental views of thousands of tweets and comments from facebook campaign posts and to track out which ones are indicating negative or positive attitude of audience towards your brand identity. On the other hand, NLP executes consistent and effective tasks of structuring unstructured form of data because human language is very complex and ambiguous for machines and these machines are entirely dependent upon logical and extremely structured form of information. NLP creates the bridging pathway between smart machines and complex language by using machines learning algorithms and statistics method that can smartly interpret natural type of language and provides appropriate responses.
Working Methodology Of Natural Language Processing – NLP System
NLP uses the standards of computer science and linguistics to understand the actual meaning of text and voices after predicting different parameters like language semantics, language syntaxes, pragmatics and morphology and then converts this linguistic outcome into machine algorithms to solve prospective tasks. NLP works by taking input of words and then break down them into simplest form just to identify its patterns and the relationships between words.
Types Of Levels And Techniques Used In NLP
Now we will talk about two major levels of NLP i.e. semantic and syntactic and its sub parts.
- Semantic:
NLP semantic analysis concentrates on to identify the actual meaning of text and voice based data by differentiating the words and relationship between them, trace out the proper meaning of sentences that may have different definitions. It can help to recognize the core objective of topic for e.g. any article consist words investment advice, investment planning, investment suggestions etc would be signifies as investment sector topic.
Sub parts of semantic analysis are:
- Word Sensing Disambiguation:
Depending upon varied situations, different word can have different meanings, for e.g.:
You should take care of your son!
You should wake up every day before sun rise!
There are two different analysis methods comes under Word Sense Disambiguation i.e. Knowledge Based or Dictionary Approach and Supervised Approach. Knowledge based approach refers to understand meaning by observing the definitions from dictionary of uncertain words within the sentence while the concluding part requires the trained form of data based on machine learning algorithms.
- Extraction Of Relationship:
In relationship extraction analysis focuses on to identify concerned relationship between more than one entity in given form of sentence & these entities can be any person name, name of places, business organizations or landmark names. For e.g.: “Rahul lives in New Delhi ”. In this sentence a person rahul is related to a place new delhi according to semantic form of category.
- Syntactic:
Syntactic analysis involves implementing the knowledge of grammatical rules in natural language processing with core objective of revealing the structure of words in sentence. Syntactic analysis also known as parsing or syntax analysis.
Sub parts of semantic analysis are:
- Sentence Tokenization Process:
Tokenization process is used to break down string of words into meaningful pieces or units of information, which we symbolizes them as tokens. We use sentence tokenization process to divide sentences and it works like defining boundaries of understanding tokens, where a token starts and ends. However, high level tokenization process can also be performed to handle more complex set of word structures. Tokenization makes a text more simple and easy to handle, and it’s the most basic task in text pre-processing.
Example of word tokenization: Customer support executive couldn’t resolve your query = [“customer support executive”, “could”, “not”, “resolve”, “your”, “query”]
- Speech Tagging Analysis:
Speech tagging involves adding speech categories into each token or unit within specific sentence. Important speech tagging parts are verb, noun, pronoun, adjective, preposition etc and speech tagging is very important to sense the complete relationship between words within sentence or phrase.
- Dependency Analysis:
Dependency analysis is a method to spot out the inter connection of words with each other within a sentence. So therefore, it analyzes the way how main words are concerned and then how becomes modify by other words respective words in order to understand the structure of entire sentence.
- Lemmatization & Stemming:
When we generally speak or write sentences, we often use inflected types of words (words derived from others). Lemmatization and stemming are two similar NLP tasks that consist of reducing words to their base form so that they can be analyzed by their common root. For example, if we apply this lemmatization to “African elephants have 4 nails on their front feet”, the result will be: African elephants have 4 nails on their front feet = [“african”, “elephant”, “have”, “4”, “nail”, “on”, “their”, “foot”]. This example is useful to see how the lemmatization changes the sentence using its base form (e.g. “feet” was converted into “foot). On the other hand, stemming trims the beginning and ending of the words to make it semantically correct. For example, using stemming for the words “consult”, “consultant”, “consulting”, and “consultants”, would result in the stem “consult”. The difference between these two approaches is that lemmatization is dictionary-based and can choose the appropriate lemma based on context, while stemming operates on single words without considering the context.
- Removal Of Stopwords:
In this process we generally filter out the high frequency words that have little or no semantic meaning for a sentence and few examples of stopwords are which, at, to, for etc. It is important to remove stop words for pre-processing of text data for creating natural language processing models. For example, I am facing problem logging with my password”, and it would be useful to remove stop words like “I”,“am”,“facing”,“my”, so you are just left with the words which helps you identify the topic: “trouble”, “logging with”, “new”, “password”.
Final Words:
Natural language processing is changing the way of analyzing voice or text based data by developing intelligent and powerful machines and to interactively make sense of varied form of human speech and it has given the vast opportunities of analyzing unstructured form of huge data.
