NLP Solutions
Implement NLP solutions to improve your business outcomes. Unlock the full potential of your text data.
Business benefits
Grow your business and improve operations with computer vision solutions
- Why should you consider AI computer vision services?
- About computer vision services
In order to develop an effective digital product, it is necessary to understand and find a way to answer users’ needs.
According to them, we implement the right high-tech solution which will boost the efficiency of processes as well as user experience.
To ensure a successful implementation you need to cooperate with a trusted partner, experienced, and strict in following reliable methods.
At Addepto, we offer computer vision services that perfectly match the unique needs of our clients. Open your business to new, high-tech opportunities.
A short guide to applying Computer Vision services
If Artificial Intelligence mimics the human way of thinking, computer vision solutions replicate the complexity of the human vision system. That is the most straightforward way of explaining computer vision, but let’s dive deeper to get a more technical-oriented explanation of the term.
Computer vision (CV) is an artificial intelligence-based technology that allows computers to observe the world, and derive meaningful information from digital images, videos, and other visual inputs.
Thanks to computer vision AI algorithms, computer vision can understand the context visual objects appear, accurately identify and classify them, and react to what they “see.” Thus, by analyzing visual data, this innovation can almost perfectly understand a particular situation and, without missing any factors, find the best solutions or suggest the most reasonable actions.
Computer vision is used in various industries- from energy and utilities to manufacturing, and automotive – and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.
However, to be helpful in business, computer vision solutions need lots of data to self-train. They analyze data repeatedly, learning to discern distinctions and ultimately recognize images. How is it possible? Two essential technologies are used to accomplish this: a subset of machine learning called deep learning and a convolutional neural network (CNN).
If computer vision applications “digest” enough data, they will learn to distinguish one image from another even better than a human can.
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
What technologies do we use?
- Programming languages
- Database
- Frameworks
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 – 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 – 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 – 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 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 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 – 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
Examples of computer visions services
Glossary
All you need to know about 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.
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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Addepto on the list of top 10 AI consulting companies by Forbes.
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
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Przemysław Piekarz Sales Analysis Manager – InPost