It focuses on natural language processing (NLP) and machine learning (ML) domains. It uses a variety of algorithms and data preprocessing techniques to identify the meaning of a text.
Mole guru is capable of creating predictive models and performing deep learning algorithms. This allows you to improve the accuracy and flexibility of your Index-Copilot.
Having previously suggested terms, the person who classifies audits them and can select proposed terms.
The terms suggested by the Index-Copilot are consistent. Some are very specific and others more general. Diversity of terms in the available vocabulary are used more assertively.
People accept or reject suggested terms, which gives feedback to the system to improve future proposals.
Our Index-Copilot articulates components and processes for automated text labelling. It is a workflow that achieves efficient results through adaptable, limited and fast management cycles.
The first step is to build a set of tagged documents for fast and efficient model training. We have different analysis modules that allows selecting terms and entity extraction schemes that best suits the document collection and the project's field of knowledge.
The next step is to optimize the level of assertiveness of the annotations, improving the possibilities of differentiation and identification of the labels used. We use terminology bases focused on each domain and field of knowledge. With semantic modelling, we enhance your vocabulary of terms.
The last step organizes and places each document in thematic zones. Our knowledge bases suggest conceptual associations and recommend new tags.
Your smart Index-Copilot is ready!
This is an example of Argentine judicial regulations.
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