Unstructured data is data that is not stored in a fixed record length format. This includes documents, social media feeds, digital pictures, and videos, etc. 80% of most organizational data is unstructured. This article aims to explore basic steps of Natural Language Processing.
What is Natural Language Processing?
Natural Language Processing is a branch of artificial intelligence that deals with how computers communicate with human beings through a natural language. It gives the computer the ability to understand interpret and utilize a human language.
What are the applications of Natural Language Processing?
Natural language processing is the driving force in the following applications;
- Speech Recognition
- Machine translation
- Search automation
- Survey analytics
- Messenger bots
How can Natural Language Processing improve my business?
Most businesses have online platforms or manual forms through which they receive customer feedback on their products and services. The feedback is in terms of texts and audio clips which are generally unstructured. Knowledge on what customers say about a particular brand helps a company provide great customer experience and respond to any challenges that may affect their clients. Natural Language Processing is used in applications such as chatbots that interact with clients directly. NLP is further used to analyze customer responses. Speech recognition can be used as an authorization code/password to give access to a company service since an audio print is unique to each client.
Summary of basic steps in Natural language processing for text analysis.
- Structure Extraction-identifying fields and blocks of content based on tagging.
- Identify and mark boundaries- this includes sentence, phrase and paragraph boundaries which act as breaks within which the analysis is conducted.
- Language identification- This helps to determine which linguistic algorithms and dictionaries to use.
- Sentence segmentation-breaking down the text to individual sentences. This assumes that each sentence is a separate idea.
- Tokenization- dividing up the characters of a sentence into tokens (Words, punctuation, identifiers, and numbers)
- Predicting parts of speech for each token- figuring out the role of each token in the sentence.
- Lemmatization- This involves figuring out the most basic form of each word in a sentence.
- Identifying stop words-these are words that one may consider filtering before performing any statistical analysis. They are words that appear way more frequently than other words.
- Dependency parsing- this involves looking at how the words in the sentence relate to each other.
- Finding noun phrases- Use dependencies from the above step to automatically group together words that are talking about the same thing.
- Named Entity Recognition- identifying and extracting names, places etc. in order to simplify downstream processing. The goal is to detect and label these nouns with the real world concepts they represent.
- Co-reference resolution-checking out for sentences that often refer to previous objects. To achieve the highest possible coverage, it’s important to identify these references and resolve them.
Some python libraries for Natural Language processing.
- Stanford Core NLP python
How computers understand Human Language
Natural Language Processing (NLP) Techniques for Extracting Information