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Natural Language Processing

Documents and text tagging for NLP model training

Our service of Natural language Processing

Documents and text tagging for NLP model training

Exploring language block analysis

At Ingedata, we quickly set up and develop teams to analyze social media conversations, customer support conversations, news feeds, internal and external knowledge bases, legal contracts, and more.

Ingedata’s solutions are generally used to train machine learning algorithms prior to deployment, to handle edge cases not handled by the algorithm during production, or to systematically evaluate the quality of an algorithm at different stages of its lifecycle.

Natural language processing (NLP) algorithms must be trained from blocks of language parsed by humans. NLP is the technology that aims to automate chatbots, autocomplete search, virtual assistants, social media monitoring and many other business situations.

A Dedicated Team to analyze the conversations

Our natural language solutions help you develop models with strong human input and deploy them at scale. From building your personal data collection teams to annotating documents, Ingedata’s tools enable you to do more in less time, by managing edge cases not handled by the algorithm during production, or by systematically evaluating the quality of an algorithm at different stages of its lifecycle.

Ingedata’s Natural Language Processing helps organizations implement and maintain a strong, well-functioning language analysis process. Our expertise in creating, using and analyzing language content is utilized to analyze social media conversations, customer support conversations, news feeds, internal and external knowledge bases, legal contracts.

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)

Natural Language

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.
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With more than 100 projects, our know-how in production management and quality assurance is based on proven methodologies in the most demanding industries.

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At Ingedata, your projects are designed and built in-house, from our secure production centers.

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Dedicated teams

Ingedata's annotators have a bachelor's degree, an engineering degree or a doctorate in your field.

All our teams work from our production centers and adapt the preparation of the data to your requirements.

Datasets specific

We collect, enrich and categorize your data, manage borderline cases to build you own datasets.

Accelerate the optimization of your algorithm using data prepared just for you.

Autonomous management

Rely on a dedicated Ingedata team. Our Know-how relieves you from coordination efforts and ensures team flexibility to adapt to your specific constraints.

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