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NLP

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The NLP Lab is Heka.ai's center of expertise on Natural Language Processing (NLP).

A team of people passionate about NLP challenges and technologies

Heka.ai's center of expertise on TALN and NLP topics

The team relies on proprietary textual data sets to create Python packages that include the approaches selected by the use case. The missions of the Lab: 
  • Technology monitoring
  • Centralization and diffusion of NLP expertise within our Heka.ai team
  • Benchmark and development of new approaches to major NLP use cases
 

Our research topics

Topic Modelling

Within our solutions (e.g., Deep Review and CRM4.0), we need unsupervised detection of themes in a corpus of documents (in other words, without interpretation). This NLP use case is known as Topic Modeling.
We benchmark different methods from the literature and combinations of approaches imagined by our teams. This benchmark is based on a dataset of Google comments in French. It allows us to determine methods according to different criteria: the time required to detect themes, consistency, and gaps between the proposed pieces.
Once we identified the best approaches, we were able to integrate them into a pre-existing theme analysis package.
To complete the benchmark carried out in the NLP lab, we supervised two groups of students (from CentraleSupélec and Mines Saint-Etienne) in the framework of their final thesis on Topic Modeling subjects.
A similar approach of research and capitalization was carried out on related use cases:
Supervised theme detection (multi-label and multi-class) in a document corpus.
Attachment of named entities to the themes detected in the document.

Textual data annotation tool

As part of our work for our Heka.ai solutions and client engagements, we sometimes need to annotate textual data: named entities, themes, sentiments, etc.
This is why we are developing a textual data annotation tool. The challenge is identifying helpful annotation types and building an architecture that can interface itself with any kind of database and environment.
Thanks to a graphical interface accessible to each user profile, the tool allows functional experts to annotate textual data in an advanced way: multi-label management, the possibility to label a subset of the text, automated multi-annotator reconciliation, and administration of
annotation campaigns with access management to the user's mesh.

Topic Detection

The first step is to detect the themes in an unsupervised way with Topic Modeling and a certain number of verbatims annotated thanks to our annotation tool. The next logical step is to move from an unsupervised model to a supervised way with Topic Detection.
For this purpose, we have carried out a benchmark of the existing approaches in the literature and new methods imagined by the team to solve supervised topic detection problems.
This benchmark has been realized to take into account both the detection performances with an F1-score metric and the associated training/inference times and an estimation of the minimum number of annotated verbatims needed. Thus, we can choose the most appropriate method depending on the use cases and constraints.

Our AI solutions based on NLP

Our publications

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Boosting search engine capabilities of RegReview:…

RegReview is an AI solution for compliance teams, to automate regulatory monitoring and processes, which brings together several tools, the most essential of which is a search engine operating on a compiled database of custom-built regulatory sources.

The database contains ~300k documents.

2023

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Labeling text clusters with keywords

We propose to explore several keyword extraction techniques to label text clusters obtained after a Text Clustering or a Topic Modeling pipeline. This work is following our previous articles about Topic Modeling and Text Clustering (here and here).

2023

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Synthetic data or how to share sensitive data…

For a period of six months, 5 students from Centrale Supélec and ESSEC worked collaboratively with Sia Partners on building a Python library to create fake - which we'll call synthetic - data.
But what's the point of creating fake data? How could it help organizations?

2023

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See our publications

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