Natural Language Processing

Documents and Text Tagging Services to Power NLP Model Training

Unlock the full potential of your NLP models
with expertly annotated data

Exploring Language Block Analysis

Advanced Language Block Analysis for Real-World NLP Solutions

At Ingedata, we quickly deploy expert teams to analyze complex language blocks extracted from sources such as social media interactions, customer support conversations, online news feeds, internal and external knowledge bases, and legal or contractual documents. This deep language block analysis enables the development of high-quality datasets that fuel Natural Language Processing (NLP) and machine learning models across a wide range of industries.

Our language annotation services are designed to support multiple stages of the AI lifecycle. They are used to train NLP models before deployment, to identify and resolve edge cases that algorithms may miss during real-time production, and to systematically evaluate and improve model performance over time. Since NLP systems—like chatbots, virtual assistants, predictive search engines, and social media monitoring tools—rely on accurate human-parsed data, language block analysis is essential for building models that understand real-world communication with context and precision.

A Dedicated Team to Analyze Conversations

Custom Teams to Interpret and Annotate Complex Language Data

Ingedata creates and deploys specialized teams to analyze conversations across multiple formats, from social media interactions and customer service transcripts to legal contracts, knowledge bases, and news feeds. Our natural language solutions are designed to bring strong human input into your NLP pipeline—whether you're developing a new model or refining an existing one. We support organizations in handling edge cases that algorithms struggle with during production, and we provide structured feedback loops to evaluate model quality throughout its lifecycle. By building your own dedicated team of linguistic experts, we help you streamline data collection, improve annotation speed, and maintain the highest levels of data accuracy.

Chatbot improvement for a major luxury brand

Implementation of a continuous improvement cycle for a unique customer experience :

  • Collected data on past conversations between customers
  • Evaluation of the relevance of the answers provided by the chatbot.
  • Identification of recurring question patterns for which the chatbot has not yet been trained.
  • A proposed roadmap for future functionality.

Natural Language

Natural language Expertise

CLASSIFICATION

NAMED ENTITIES

SPEECH-TO-TEXT

SENTIMENT ANALYSIS

Classification

  • Used to sort various texts, depending on what they talk about from a macro point of view (what does the text talk about? )
  • The task consists in reading a text and assigning one or several classes to it
  • Several classes can be used on the same text in order to refine the information (e.g. with 2 classes: the text talks about “France” AND “Law”)
  • Can be combined with sentiment analysis (the text talks about “France” AND “Law”, with the sentiment “Anger”)
  • Text classification is useful to assign tags to texts, so they can then be better filtered
  • Of course, text classification can be easy or complex. It is not the same to classify texts about “cats & dogs” or finance topics. Domain expertise might be required to understand the texts.

Classification

Named Entities

  • Contrarily to text classification that categorizes texts, named entity recognition is about looking at what’s inside the text – from a micro perspective.
  • The task is not about saying “this text talks about…” but “this sentence, at this location in the text, talks about…”
  • In named entity recognition, we must segment (isolate) the keyword or key sentence of interest and categorize it depending on what it talks about
  • This allows finer analysis of the text in a contextual manner
  • A famous example : the sentence “apple has a stock price of XXX$” allows to understand that we are talking about the company Apple and not the fruit Apple. This is because we didn’t only recognize the word “Apple”, but also because we could relate it the other named entity “stock price”

langage-naturel

Speech-to-text

  • It is the first step of text analysis when the input is audio (text). Audio speech must be converted into a text prior to analyzing the content of the text
  • Mostly known in voice assistants such as Alexa (Amazon), Siri (Apple) and “Hey Google”
  • The task consists in writing down a text that was initially a speech.
  • One of the challenges is to understand different accents and ways of speaking (e.g. slang language)
  • Speech to text services must be available in different languages. At Ingedata, French, English, German, Italian and other on-demand languages are available (e.g. Asian languages) It is the first step of text analysis when the input is audio (text). Audio speech must be converted into a text prior to analyzing the content of the text
    Mostly known in voice assistants such as Alexa (Amazon), Siri (Apple) and “Hey Google”
    The task consists in writing down a text that was initially a speech.
    One of the challenges is to understand different accents and ways of speaking (e.g. slang language)
    Speech to text services must be available in different languages. At Ingedata, French, English, German, Italian and other on-demand languages are available (e.g. Asian languages)

speech-to-text

Sentiment Analysis

  • Sentiment analysis is used to differentiate the sentiment of writers, most times on social media comments or similar media (forums…)
  • The challenge in Sentiment Analysis is to handle subjectivity when analyzing sentiment
  • We need to objectify as much as possible, by using sentiment definitions by researcher Robert Plutchik :
  • In order to prevent subjectivity, we also use consensus, where several people give their analysis of the sentiment to then compare results
  • Amount of consensus is calculated using a scientific method: Fleiss’ kappa (or sometimes Cohen’s kappa for simpler cases)

Sentiment-Analysis

CLASSIFICATION

Classification

  • Used to sort various texts, depending on what they talk about from a macro point of view (what does the text talk about? )
  • The task consists in reading a text and assigning one or several classes to it
  • Several classes can be used on the same text in order to refine the information (e.g. with 2 classes: the text talks about “France” AND “Law”)
  • Can be combined with sentiment analysis (the text talks about “France” AND “Law”, with the sentiment “Anger”)
  • Text classification is useful to assign tags to texts, so they can then be better filtered
  • Of course, text classification can be easy or complex. It is not the same to classify texts about “cats & dogs” or finance topics. Domain expertise might be required to understand the texts.

NAMED ENTITIES

Named Entities

  • Contrarily to text classification that categorizes texts, named entity recognition is about looking at what’s inside the text – from a micro perspective.
  • The task is not about saying “this text talks about…” but “this sentence, at this location in the text, talks about…”
  • In named entity recognition, we must segment (isolate) the keyword or key sentence of interest and categorize it depending on what it talks about
  • This allows finer analysis of the text in a contextual manner
  • A famous example : the sentence “apple has a stock price of XXX$” allows to understand that we are talking about the company Apple and not the fruit Apple. This is because we didn’t only recognize the word “Apple”, but also because we could relate it the other named entity “stock price”

SPEECH-TO-TEXT

Speech-to-text

  • It is the first step of text analysis when the input is audio (text). Audio speech must be converted into a text prior to analyzing the content of the text
  • Mostly known in voice assistants such as Alexa (Amazon), Siri (Apple) and “Hey Google”
  • The task consists in writing down a text that was initially a speech.
  • One of the challenges is to understand different accents and ways of speaking (e.g. slang language)
  • Speech to text services must be available in different languages. At Ingedata, French, English, German, Italian and other on-demand languages are available (e.g. Asian languages) It is the first step of text analysis when the input is audio (text). Audio speech must be converted into a text prior to analyzing the content of the text
    Mostly known in voice assistants such as Alexa (Amazon), Siri (Apple) and “Hey Google”
    The task consists in writing down a text that was initially a speech.
    One of the challenges is to understand different accents and ways of speaking (e.g. slang language)
    Speech to text services must be available in different languages. At Ingedata, French, English, German, Italian and other on-demand languages are available (e.g. Asian languages)

SENTIMENT ANALYSIS

Sentiment Analysis

  • Sentiment analysis is used to differentiate the sentiment of writers, most times on social media comments or similar media (forums…)
  • The challenge in Sentiment Analysis is to handle subjectivity when analyzing sentiment
  • We need to objectify as much as possible, by using sentiment definitions by researcher Robert Plutchik :
  • In order to prevent subjectivity, we also use consensus, where several people give their analysis of the sentiment to then compare results
  • Amount of consensus is calculated using a scientific method: Fleiss’ kappa (or sometimes Cohen’s kappa for simpler cases)

Our Customers are satisfied

Let’s Build Your NLP Dataset

Meet Your Future Data Labeling Team

No matter where you are in your AI journey—just starting out or scaling a mature NLP system—Ingedata provides the expertise, tools, and people to help you succeed. Reach out to us today for a custom proposal and see how our text annotation and document labeling services can power your next NLP breakthrough.