NLP and its most innovative uses

What is Natural Language Processing (NLP)?

NLP (Natural Language Processing) is a technology incorporated within the field of Machine Learning that enables computers to interpret, manipulate, and understand human language.

Many companies use NLP Software to process, analyze, and sometimes respond to messages coming from communication channels such as emails, social media news feeds, text messages, and more.

Why is it important?

NLP is essential for in-depth analysis of all text and voice data efficiently. One of its most innovative features is that it adapts and interprets various differences in dialects, jargon and grammatical irregularities typical of the language that is used in everyday life. For this reason, natural language processing is considered one of the most complex fields of computer science. The language is loaded with double meanings and in order to understand it, it is necessary to have extensive knowledge of the context in which it is used.

How does it work?

All natural language processing methods take into account the hierarchies that define the relationships between words. Therefore, to train the algorithm in the language, the following areas of linguistics are used:

Morphology –
It deals with the composition of words and their relationships with other words.

Syntax –
Defines the way words are put together to form sentences.

Semantics –
Refers to the meaning of words and groups of words.

Pragmatics –
Refers to the context in which linguistic locutions are carried out.

Phonology –
It’s the phonetic structure of spoken language.

1st step

Part-Of-Speech Tagging (POST) –
It is related to morphology: determining the function of each word individually. The difficulty lies in the fact that the words can change function depending on the sentence in which they are placed. To solve this, one can resort to methods such as the use of extensive corpora of texts such as the British National Corpus, which are made up of millions of labelled words and from which learning rules for word labelling can be deduced. Nowadays, automatic tagging programs use learning algorithms, which means that they automatically understand the rules of existing text corpora and use them to define other words functions.

Reference Towards Data Science

2nd step

Parse trees –
In this step, knowledge gained from syntax is used to understand the structure of sentences. To do this, syntactic analysis diagram are used with which sentences can be divided into phrases called parse trees.

Reference NLTK

3rd step

Semantics –
A word can have several possible meanings. Therefore, an attempt is generally made to determine the meaning of a word with the help of the words that precede or follow it. These differences can be learned through the use of text corpora, in which the meaning of each word is accurately reproduced.

Reference Papers With Code

What NLP services are there?

• Sentiment analysis
• Document processing
• Chatbots and virtual assistants
• Text classification
• Named Entity Recognition (NER)
• Natural Language generation

What tools are used for NLP?

MonkeyLearn –
It is a platform based on NLP that makes it possible to obtain valuable information from texts. It has pre-trained models to perform text analysis tasks, such as sentiment analysis, topic classification or keyword extraction. It is also possible to create custom Machine Learning models that are tailored to a particular business.

Natural Language Toolkit –
The NLTK (Natural Language Toolkit) is a Natural Language Processing library that uses the Python programming language. NLTK is free software, which allows students and academic staff to carry out studies with the tool without the need to make an economic investment. This tool is also open source, which  makes it ideal for expanding its functionality if needed.

Aylien –
Aylien is a SaaS API that uses deep learning and NLP to analyze large volumes of text-based data, such as academic publications, real-time media content, and social media data. It is useful for tasks such as: text summarization, article extraction, entity extraction, and sentiment analysis, among others.

IBM Watson –
IBM Watson is a suite of AI services stored in the IBM Cloud. One of its key features is a natural language understanding, which allows you to identify and extract keywords, categories, emotions, entities, and more.

Google Cloud –
The Google Cloud Natural Language API provides several pre-trained models for sentiment analyses, content classification, entity extraction, and more. In addition, it offers AutoML Natural Language, which allows you to create custom Machine Learning models. As part of the Google Cloud iftrastructure, it uses Google’s language understanding and questions answering technology.

Amazon Comprehend –
It is an NLP service, integrated with the Amazon Web Services infrastructure. It is used for tasks like sentiment analysis, theme modeling, entity recognition, and more. In healthcare, there is a specialized variant: Amazon Comprehend Medical, which allows you to perform advanced analysis of medical data using Machine Learning.

Standford Core NLP –
This is a popular library built and maintained by the NLP community at Standford University. It allows you to perform a variety of NLP tasks, such as part-of-speech tagging, tokenization, or named entity recognition. Some of its main advantages include scalability and speed optimization, making it a good choice for complex tasks.

TextBlob –
This is a Python library that works as an extension to NLTK, allowing you to perform the same NLP tasks in much more intuitive and user-friendly interface. It’s a good choice for beginners who want to tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more.

Spacy –
It is an open source Python NLP library. It is designed to support large volumes of data. It has a series of pre-trained NLP models.

GenSim –
It is a Python library that largely deals with topic modeling tasks using algorithms like Latent Dirichlet Allocation (LDA). It is also used to recognize text similarities, index texts, and navigate through different documents. This library is fast, scalable, and good at handling large volumes of data.

In which companies is NLP used?

• Eli Lilly is a multinacional pharmaceutical company that uses natural language processing to help its more than 30,000 employees globally share accurate and timely information internally and externally. They have developed Lilly Translate, a solution that uses NLP and deep learning to generate content translation through a validated API layer. The Lilly Translate service provides real-time translation of Word Excel, PowerPoint and text for users and systems, while keeping document formatting in place. Its deep learning language models help improve translation accuracy, and refined language models are being created that recognize Lilly-specific terms and industry-specific technical language while maintaining the format of regulated documentation.
• Accenture uses NLP for legal analysis. They own a project: Accenture Legal Intelligent Contract Exploration (ALICE) that helps the firm’s legal organization perform text searches on its more that one million contracts, including contract clause searches.
• Verizon’s Business Service Assurance Group uses NLP and Deep Learning to automate the processing of customer request feedback. The group receives a large volume of incoming requests each month that needed to be read and acted on individually until Global Technology Solutions, Verizon’s IT group, developed Digital Worker. Digital Worker uses network-based deep learning techniques and NLP to read repair tickets that are submitted from Verizon’s web portal and emails. It automatically responds to requests such as current ticket status reports or repair progress updates. The most complex problems are sent to human engineers.
• Great Wolf Lodge (a hospital and entertainment chain) analyses the feedback from its monthly surveys and determines whether the writers are likely to be a network promoter, detractor, or neutral party. The AI was trained specifically for hospitality on more that 67,000 reviews. It runs in the could and uses internally developed algorithms, then identifies key elements that suggest why respondents feel the way they do about GWL.
• Google introduced in 2019 the Machine Learning model BERT (an acronym for Transformer Bidirectional Encoder Representations). BERT is an open-source technology developed by Google and based on neural networks, which allows training natural language processing (NLP) models with a sophistication never achieved before. This helps the search engine algorithm to better understand searches, both at the query and content level. It is constantly evolving through the models and data that is sent to it.

¿Where you aware of NLP?

–

If you are looking for more information, get in touch with us.

References:

Nexcode
Cio
Papers with code
Towards Data science
NLTK