NLP Solutions


Implement NLP solutions to improve your business outcomes. Unlock the full potential of your text data.


Business benefits

Natural Language Processing: What do you need to know?


Natural Language Processing
NLP systems
Implementation

Natural language processing (NLP) is a high-tech solution that enables computers to extract meaning from text


To do this, NLP software uses machine learning and artificial intelligence technologies.

The main goal of natural language processing is for computers to achieve advanced text comprehension at a level of understanding similar to that of humans. Why?

When computer systems reach this level, they can:

  • Fully understand human languages,
  • Draw conclusions from the text,
  • Translate and summarize,
  • Generate natural human language and text.

Natural language processing will give your company the ability to quickly develop while making full use of your data. NLP solutions provide the necessary tools to analyze both numerical and linguistic data.

Discover the NLP solutions that best suit your business needs.


How does natural language processing work?


Natural Language Processing systems are usually based on machine learning algorithms

Rather than manually coding large sets of rules, NLP can rely on machine learning to learn these rules automatically by analyzing a set of examples and drawing statistical conclusions.

Learn more about NLP algorithms.


Why should you entrust Addepto with the implementation of NLP solutions in your company?


At Addepto, we believe that continual improvement, not staying in the same place, is key. Advanced technologies offer many solutions for development.

Our team consists of experts in the field of NLP, data science, AI and machine learning. We have experience in working on NLP solutions for international companies.

Get in touch with our experts to find out more about our services and decide if we fit your business.



NLP applications in different industries



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NLP can analyze thousands of shipping documents


NLP can provide manufacturers with greater visibility into the areas of their supply chain that are lagging behind.

Some of the most popular NLP solutions in the manufacturing industry include:

  • Automation of manual processes
  • Industry benchmark data collection
  • Reducing language barriers
  • Perform compliance tracking
  • Real-time tracking of data changes

Financial institutions such as banks can gain valuable insights through data analysis


The most popular NLP solutions in the financial sector include:

  • Faster identification of money laundering and other forms of fraud
  • Big Data analysis and market research
  • Obtaining business insight based on the analyzed data
  • Risk minimization and risk management improvement
  • Improving the decision-making process.

Retailers can gain a lot from introducing NLP solutions to their business


Their customer data can be analyzed and, based on conclusions, used to improve the quality of service and customer loyalty.

The most effective NLP solutions include:

  • Data-driven decision making
  • Better marketing results
  • Identifying the most profitable customers and improving personalized offers
  • Understanding the needs of your customers
  • Strengthening brand exposure


Manufacturing
Finance & Insurance
Retail

Technologies that we use



Programming languages

Database

Frameworks


Python


Python – Python is considered the most popular programming language in the Data Science area mostly because of its quite straightforward and easy-to-read syntax. Still, the benefits of using it in building Machine Learning solutions are numerous. This language has a large and active community that develops and maintains a wide range of libraries and frameworks specifically for Machine Learning and Artificial Intelligence, which provide pre-built algorithms and tools for building and training models. Python is a versatile and flexible language; it can be used in scientific computing and web development, which makes it a great choice for building ML models, often requiring a mix of programming, data analysis, and visualization.

R


R – With built-in statistical functions, R was made specifically for machine learning applications. This language also has an extensive library of data visualization tools, which makes it easy to create charts, graphs, and other visualizations of data. Moreover, R has an interactive console that allows users to explore data and experiment with different analyses and models in real time, enabling Data Scientists to test different models and approaches. R - as well as Python - can be easily integrated with other tools and languages, such as Python, SQL, and Hadoop.
MongoDB


MongoDB – MongoDB – a document-oriented database that stores data in JSON-like documents with the dynamic schema. It is great for transactional stores where performance is a concern. Its schema-less operations allow you to update the data on the fly.

PySpark


PySpark – PySpark – used to transform data. It enables you to run AI applications on billions of data on distributed clusters 100 times faster than the traditional python applications.
PyTorch


PyTorch – PyTorch is also an open-source machine learning library, yet it is was developed by Facebook's AI Research team. PyTorch allows Data Scientists to prototype and deploys machine learning models using Python. It is used in various applications such as computer vision, natural language processing, and reinforcement learning.

TensorFlow


TensorFlow – TensorFlow is an open-source machine learning library developed by Google Brain Team, used to build and train machine learning models, including deep neural networks. It provides high-level APIs such as Keras that make it easy to build and train models without needing to write complex code, visualization tools, and pre-built models and datasets.

Hadoop


Hadoop – Hadoop – is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs.

Key benefits

NLP solutions will improve your business



Predictive text


NLP solutions such as autocorrect and autocomplete analyze personal language patterns and determine the most appropriate suggestions for individual users.


Smart assistants


Virtual smart assistants, such as easily recognizable Siri or Alexa, use NLP technology to understand human inquiries and respond to their needs.


Search engine results


Search engines use NLP to better understand what users are looking for and be able to find relevant information faster.


Text classification


Text classification is critical to automating the understanding, processing, and categorization of unstructured text. NLP models allow you to organize data using predetermined tags.


Text extraction


Natural language processing is able to automatically identify specific keywords, product names, descriptions, etc. within text.


Text summarization


NLP can quickly extract crucial information from the text and summarize it based on key phrases from text or determined inferences.


All you need to know about NLP


How do companies take advantage of NLP solutions?
What is NLP used for?
What are NLP solutions?
What are NLP tools?
What does NLP stand for in AI?
What are the phases of NLP?
What is the main goal of NLP?


How do companies take advantage of NLP solutions?


Apple

Siri, Apple’s smart assistant, uses NLP to help it recognize speech patterns and understand contextual cues.

Thanks to natural language processing, intelligent assistants like Siri can improve our daily life by making things like ordering specific products faster and easier.

Customers have noticed the benefits of this NLP solution, and in the US alone, Siri has approximately 375 million active users every month!

Increasingly, devices of everyday use such as light switches, cars, food processors, etc. implement solutions based on NLP technology. This trend will only increase in the future.

“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” – Amy Stapleton, Analyst, Opus Research

Amazon

Companies like Amazon are aware of the business opportunities that come with natural language processing.

Amazon Comprehend uses NLP solutions to extract information from text documents.

This technology provides insight by recognizing language, titles, key phrases, and many other basic elements of text documents.

Google

Another company that uses NLP solutions to improve its services is Google. Its app, Google Translate, uses natural language processing technology to offer high-quality translation services worldwide.

Google Translate is used by 500 million people every day, so they can understand over 100 different languages.

Such NLP applications improve communication between people from different countries and break down language barriers. It enables individuals to understand foreign documents, catalogs and even technical manuals.

Google’s strengths in areas like search, geographical information, image recognition and natural language processing, make its potential for assistive technologies enormous.

“I think we’re at the forefront of that development, we’re pushing hard and getting there.” – Sundar Pichai, Google CEO

What is NLP used for?


NLP is used for language analysis both in text and speech. NLP solutions assist humans in everyday activities like understanding foreign languages, emailing, and text categorization.

What are NLP solutions?


NLP enables computers to perform language-related tasks and interact with humans.

The most effective NLP solutions include:

  • Text classification
  • Text extraction
  • Text summarization
  • Predictive text
  • Smart assistants
  • Search engine results

What are NLP tools?


Some of the most popular NLP tools are:

  • NLTK (Natural Language Toolkit)
  • Aylien
  • Stanford Core NLP
  • SpaCy
  • TextBlob
  • Apache OpenNLP
  • GenSim

What does NLP stand for in AI?


AI (Artificial Intelligence) is a wide field of studies that focuses on how machines can understand our world. NLP is just a branch of AI, focusing on understanding human language.

What are the phases of NLP?


We can distinguish five phases of NLP:

  • Lexical Analysis
  • Parsing (Syntactic Analysis)
  • Semantic Analysis
  • Discourse Integration
  • Pragmatic Analysis

What is the main goal of NLP?


The main goal of natural language processing is to achieve a human-like level of language processing. It should be able to make sense of the text and perform text classification, translation, etc.

NLP is facing challenges in understanding and modeling different elements in variable contexts.


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