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Natural Language Processing

1. What is NLP?

Natural Language Processing, also known as NLP, is a branch of artificial intelligence that deals with computers and human interaction using the natural language.

It focuses on data science and human language, and is scaling to lots of industries. NLP is growing by the day, thanks to the huge improvements in data access and the increase in computational power. This allows practitioners to achieve meaningful results in areas like healthcare, BFSI and human resources, among others.

2. How does NLP work?

Language often has various meanings and understanding the difference requires a considerable amount of knowledge of the content where the words are used. 

In spite of these difficulties, computers are enhancing their understanding of human language and its complications. To pace it up, computer linguists rely on the knowledge of various traditional linguistic fields:

  • The term morphology is the study of words and its relationship with other words
  • Syntax defines how different phrases and words are arranged to create a meaningful sentence
  • Semantics is the understanding the meaning of words and phrases
  • Pragmatics is concerned about the context and content of spoken expressions
  • Phonology deals with the phonic structure of spoken language

3. What are natural language processing techniques?

Named Entity Recognition

The most elemental technique in NLP is identifying and pulling out the entities in the text. They zero in on the basic concepts and references in the text. Named entity recognition (NER) extracts entities such as individuals, locations, institutions, etc. from the text.

NER output for the text will be:

Individual: Maria Oliver, Emma Rose

Location: California, Arizona

Institution: ASU

NER is based on supervised models and grammar. However, there are some platforms such as open NLP that have pre-trained and built-in NER models.

Sentiment Analysis

Sentimental analysis is the most extensively used technique in NLP . It is most useful for use-cases like surveys, customer reviews, and social media comments. The basic output of sentiment analysis is a 3-point scale: positive/negative/neutral. In complicated cases the output can be a numeric score that can be segregated into various categories.

Sentiment Analysis can be performed using both supervised and unsupervised techniques. Naïve Bayes is a prominent supervised model used for sentiment analysis. It is a probabilistic classifier with assumptions on conditional independence among features. A model is trained and then used to identify the sentiment. Different machine learning techniques like random forest or SVM can also be used.

Text Summarization

There are some techniques in NLP that help in giving a synopsis of large chunks of text. This is widely used in cases such as research and news articles.

The categories in text summarization include extraction and abstraction. Extraction methods create a summary by taking excerpts from the text. 

For eg: 

Source text: Chloe and Mark took a bus to visit a church nearby. In the church, Mark received a phone call and rushed to the hospital

Summary: Chloe and Mark visit church. Mark rushed to hospital.

Abstraction creates summary by creating fresh text that explains the core of the original text. There are different algorithms that can be used for text summarization like LexRank, Latent Semantic Analysis ,etc. LexRank algorithm uses similarity and ranks the sentences. 

For eg: 

Source text: Chloe and Mark took a bus to visit a church nearby. In the church, Mark received a phone call and rushed to the hospital

Summary: Mark rushed to the hospital after visiting the church

Aspect Mining

Aspect mining identifies various aspects in the text. When used in conjunction with sentiment analysis, it takes out complete information from the text. One of the simplest methods in aspect mining is using part-of-speech tagging.

When aspect mining is used along with sentiment analysis is used on the sample text, the output delivers the intent of the text. 

There has been immense growth of customer surveys and reviews through voice or text due to  increased use of service-based industries.  Aspect mining extracts sentiments from these opinions and rates customer experience

Topic Modeling

Topic modeling is one of the most complex methods to identify natural topics in the text. Topic modeling is an unsupervised technique where a labeled training dataset and model training are not required.

It scans a set of documents, detects patterns and automatically clusters words and expressions that describe those sets of documents.

4. What is NLP used for?

Improving user experience

You can integrate NLP in a website to provide a more user-friendly customer experience. Features like spell check, predictive text and autocorrect makes it easier for users to search for information. This also keeps them from navigating away from your site.

Automating support

Chatbots are advancements in NLP, increasing their usefulness so that agents don't have to be the first point of communication. Some features include being able to help users navigate the website, order products or services, and manage accounts.

Monitoring and analyzing feedback

In social media, surveys, forms, support tickets, etc customers are leaving feedback about the product or service. NLP helps in aggregating and extracting valuable information of the feedback and turning it into insights that can help improve the organization.

5. How to implement NLP?

Below is the procedure on how the computer understands natural language.

a. Sentence Segmentation

Firstly, break the chunk of text into separate sentences. It becomes easier to develop understanding for a single sentence as compared to a paragraph. You can split sentences whenever there is a punctuation mark (full stop, colon, etc). Sentence segmentation can also be carried out for a poorly formatted text.

b. Word Tokenization

After splitting the text into different sentences, break the sentences into separate words. These words are called tokens. Tokenization can be done by splitting sentences to words whenever there is space or punctuation.

c. Predicting Parts of Speech for Each Token

Each token will be identified as noun, verb, adjective etc. This will help in identifying the real intent of the sentence.The words and the surrounding phrases can be sent to the Part of Speech tagging and classification model. This is a statistical  model based on millions of sentences whose tokens are tagged as a part of speech.

d. Text Lemmatization

Lemmatization is basically finding the dictionary phrase for the word. For eg: 

(i) I need a battery

(ii) I need batteries

Both sentences talk about batteries, but the computer takes it as two different strings, the reason why lemmatization is done is so that It identifies the part of speech of the tokens and finds the lemma for that word.

e. Identifying Stop Words

Before going to statistical analysis, there might be words appearing a lot more frequently than the other words. Words like ‘a’, ‘and’, ‘the’, introduce a lot of noise and hence are classified as stop words so that they are filtered out during the statistical analysis. 

f. Dependency Parsing

The next step is identifying the relationship between the tokens. The traditional approach includes building a tree with the main verb being the root of the tree. Deep learning techniques are now used for finding out dependencies. It is not an accurate model, but the NLP models keep getting better by the day. Further approach also includes grouping words of the same part of speech together making identifying the meaning of the sentence much easier.

g. Named Entity Recognition (NER)

As mentioned above, NER will detect and label nouns from the text. This is used to extract important information from the document. NER uses the context of the word appearing in the sentence and a statistical model to guess which type of noun a word represents

h. Coreference Resolution

Coreference Resolution is one of the most difficult steps in the NLP pipeline. It basically tries to capture pronouns and tries to map it to a noun for which it might be used. Deep learning models have ensured more accuracy in the above process.

This a typical NLP pipeline, which can be altered based on the requirements and the structure of the NLP library. For example, some libraries perform sentence segmentation later in the pipeline. 

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