This article presents the NLP, combining linguistic, machine learning And deep learning for understanding and generating natural language. Aqsone NLP solutions reveal untapped information, maximize productivity And improve customer experience, particularly in the industrial sector.
Manel Mezghanni
Data Scientist
This blog post is the first in a series of articles on NLP that explain the challenges this technology faces, how we approach them at Aqsone, and a reflection on the future of the NLP field with new technologies. Let's start with an introduction to NLP and with an overview of the challenges inherent to this technology.
Why do machines need NLP?
A person can generate hundreds of words in a statement, with each sentence having its own complexity and contextual nuance. Allowing a machine to understand means analyzing several hundred or thousands of people and their possible statements which differ from one place to another…
This type of conversational data is called unstructured data and cannot be inserted into perfectly stacked rows and columns. The only way to teach a machine all this is to let it learn by experimenting.
What is NLP?
THE natural language processing (NLP) is divided into two key categories:
- Understanding natural language (NLU or Natural Language Understanding)
- Natural language generation (NLG or Natural Language Generation).
NLP brings together three main areas: computer science, human language and artificial intelligence. In the latter, NLP combines linguistic approaches, automatic learning (ML or Machine Learning) and deep learning (DL or Deep Learning) techniques.
Figure 1: The positioning of NLP in the Artificial Intelligence ecosystem
NLP is considered an invaluable support for artificial intelligence. It helps in establishing effective communication between computers and human beings. In recent years, there have been significant progress in the understanding of human language by computers using NLP.
NLP must include several different techniques for interpreting human language. These can range from statistical and machine learning methods to rules and algorithmics. The NLP has a immense potential in real-world application areas such as understanding full sentences and finding synonyms, speech recognition, speech translation, and writing full, grammatically correct sentences, these are just a few examples…
Challenges for implementing NLP
The main challenge is linked to the data quality coming from different sources, large, heterogeneous and complex.
Until then, computers had mainly processed structured data, that is to say data organized, indexed and referenced, often in databases.
In NLP, we deal with unstructured data. Please note that 80% company data is not structured. ( Chiang, Catherine. 2018. “In the Machine Learning Era, Unstructured Data Management is More Important Than Ever.” » Blog, Igneous, July 31. Accessed 2019-06-09.)
Examples of unstructured text data are social media posts, news articles, emails, and product reviews. To process such information, NLP must learn the structure and grammar of natural language. In this example, a lot of information such as emotion, tone, organization, etc. is included. could be extracted using NLP:
FFigure 2: an example of information extraction based on NLP techniques
Lambiguities in the data add additional challenges to contextual understanding. Semantics allows you to find the relationship between entities and objects. Entities and object extraction from text and visual data can only provide accurate information if the context and semantics of the interaction are identified. Additionally, currently available search engines can search for objects or entities rather than keyword-based searching. Semantic search engines are necessary because they better understand user queries typically written in natural language.
Another challenge is extracting relevant and correct information from unstructured or semi-structured data using information extraction techniques. There is a need to understand the capabilities and limitations of existing techniques related to pre-processing, data extraction and transformation, and representations of large volumes of unstructured multidimensional data. Increased efficiency and accuracy of these systems are important. But the complexity linked to a large volume of data that must be processed in real time poses challenges for ML-based approaches, whether it is data dimensionality, scalability, distributed computing and adaptability. Effectively managing sparse, imbalanced, and high-dimensional datasets is complex.
Conclusion
The solutions developed by Aqsone, using these NLP techniques, are relevant for many use cases. Solutions integrating ML and NLP make it possible to give value to information that was previously unexploited. More importantly, these types of solutions help businesses maximize productivity while improving customer experience. In our next article, we will explain to you how NLP could help solve many challenges in the industrial world.
So stay tuned…
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