Natural Language Processing

Have something to say? Software is listening to you. Through AI-driven NLP services, we’ve made revolutionary progress in interpreting human languages and behavior. From personalized search results to chatbots and virtual assistants, our Natural Language Processing solutions take communication beyond words.

Services

Don’t just focus on the words. Identify the intent. As a Natural Language Processing service provider, we do just that in order to model human languages and recognize the underlying meaning behind the words said or the actions performed. Raised eyebrows? Find out how.

Sentiment Analysis

As complex as human emotions and languages are, NLP based models can understand and infer the right context and undertone to gauge consumer needs. Sentiment Analysis is an opinion mining technique that can be proactively used to formulate business strategies, exceed customer expectations, generate leads, build marketing campaigns and open up new avenues for growth.

Entity Extraction

Organizations generate huge stacks of unstructured data that is information-rich, but largely ineffective. Entity recognition allows businesses to automate free-form text analysis and extract relevant content from multiple data sources. This aids the decision-making process, improves customer support, helps personalize search algorithms, and more in a fast, cost-efficient manner.

Intent Classification

By analyzing customer conversations, our team can train the model to compute sentence vectors and classify text input into various datasets. This automates the process of understanding sophisticated linguistic nuances as well as helps in identifying user intent, undertone and objectives. The result is truly conversational & competent chatbots, enhanced products and other human-machine interaction systems used for generating analytics, data processing or automating business processes.

Text Categorization

Automated text categorization helps structure critical business data and segregate it for better searchability and organization. By classifying text in this manner, automation allows you to minimize errors, scale real-time insights, save time, and extract the most value out of available information. Reduce inconsistencies and manage your documentation at a fraction of the cost!

Our Process

NLP implementation steps are often very specific to the tasks that need to be executed. Our fundamental process relies on various text and data analysis tools to simplify human-machine interactions and enable businesses to deliver next-generation digital experiences that are contextually relevant, highly interactive, and refreshingly human.

Text Preprocessing

In the first stage, we begin by collecting data from multiple sources and building a raw text corpus. Damaged, irrelevant or incomplete data is eliminated and useful text is normalized and prepared for further analysis.

Text Parsing and Exploratory Data Analysis

This is the structuring stage where the raw data is sifted and organized to do a more focused analysis with a smaller dataset. This involves identifying and removing irrelevant sections, extracting coded metadata and determining the format. By selecting the various intents and entities required for the predetermined tasks, a deep exploratory analysis helps establish a format for representation.

Text Representation and Transformation

Now that the datasets are categorized, we use various visualization techniques to represent the data in a meaningful format to retrieve useful insights. This includes a semantic, syntactic and pragmatic analysis of the text to get an overview of the interpretable content.

Modeling

We now approach the most important Natural Language Processing discipline of modeling artificial neural networks (ANN) and training them to automate the learning of complex linguistic and behavioral models. Text mining at this stage helps to funnel down the data and do targeted information retrieval.

Evaluation and Deployment

At the final stage, the NLP model is tested for performance against a number of training parameters. The metrics are observed and corrective measures are taken where necessary. The successful model is then deployed in the execution environment.

Our Process

NLP implementation steps are often very specific to the tasks that need to be executed. Our fundamental process relies on various text and data analysis tools to simplify human-machine interactions and enable businesses to deliver next-generation digital experiences that are contextually relevant, highly interactive, and refreshingly human.

Text Preprocessing

In the first stage, we begin by collecting data from multiple sources and building a raw text corpus. Damaged, irrelevant or incomplete data is eliminated and useful text is normalized and prepared for further analysis.

Text Parsing and Exploratory Data Analysis

This is the structuring stage where the raw data is sifted and organized to do a more focused analysis with a smaller dataset. This involves identifying and removing irrelevant sections, extracting coded metadata and determining the format. By selecting the various intents and entities required for the predetermined tasks, a deep exploratory analysis helps establish a format for representation.

Text Representation and Transformation

Now that the datasets are categorized, we use various visualisation techniques to represent the data in a meaningful format to retrieve useful insights. This includes a semantic, syntactic and pragmatic analysis of the text to get an overview of the interpretable content.

Modeling

We now approach the most important Natural Language Processing discipline of modelling artificial neural networks (ANN) and training them to automate the learning of complex linguistic and behavioural models. Text mining at this stage helps to funnel down the data and do targeted information retrieval.

Evaluation and Deployment

At the final stage, the NLP model is tested for performance against a number of training parameters. The metrics are observed and corrective measures are taken where necessary. The successful model is then deployed in the execution environment.

Tools & Methods

Case Studies

Other Services

We offer the full spectrum of services to assist enterprises in working better
& achieving their goals. Take a look at our other service offerings below.

Chatbots

Machine
Learning

Maruti Techlabs offers
industry-agnostic custom
chatbot development
services for customer

support, lead generation,
FAQs and more. Integrate
intelligent chatbots with
existing set of tools
across different channels.

We help enterprises process
high volume data, enable
faster decision making,
quicker anomaly detection,
business process automation
along with solving complex
challenges with our ML
services and solutions.

Other Services

We offer the full spectrum of services to assist enterprises in working better & achieving their goals. Take a look at our other service offerings below.

Chatbots

Maruti Techlabs offers industry-agnostic custom chatbot development services for customer support, lead generation, FAQs and more. Integrate intelligent chatbots with your existing set of tools across different channels.

Machine Learning

We help enterprises process high volume data, enable faster decision making, quicker anomaly detection, business process automation along with solving complex challenges with our ML services and solutions.

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