This article introduces the NLP, combining linguistics, machine learning and deep learning for the understanding and generation of natural language. Aqsone NLP solutions reveal untapped information, maximize productivity and improve the customer experience, especially in the industrial sector.
This blog post is the first in a series of articles on NLP that explain the challenges this technology faces, how we are addressing 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 in this technology.
Everything we express in written or verbal form includes a huge amount of information which goes far beyond the meaning of individual words. The combination of subject, tone, word selection, sentence structure, punctuation/phrases allows humans to interpret this information, its value, and its intent. Humans are programmed to understand and often even predict the behavior of others using this complex set of information.
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 that 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 of this is to let it learn by experimenting.
The natural language processing (NLP) is divided into two key categories:
The NLP includes three main areas: computer science, human language and artificial intelligence. In the latter, NLP combines linguistic approaches, machine learning techniques (ML or Machine Learning) and deep learning (DL or Deep Learning) techniques.
NLP is considered to be an invaluable support for artificial intelligence. It helps to establish effective communication between computers and human beings. In recent years, there have been significant advances in the understanding of human language by computers using NLP.
NLP should include several different techniques for interpreting human language. These can range from statistical and machine learning methods to rules and algorithms. The NLP has a Immense potential in real areas of application such as understanding complete sentences and finding synonyms, speech recognition, translating speech, and writing complete, grammatically correct sentences, these are just a few examples...
The main challenge is related to the data quality coming from different sources, large in size, heterogeneous and complex.
Until now, computers had mainly processed structured data, i.e. data that was organized, indexed and referenced, often in databases.
In NLP, we deal with unstructured data. Note that 80% of business 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. could be extracted using NLP:
Ambiguities in the data add additional challenges to contextual understanding. Semantics makes it possible 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. In addition, currently available search engines can search for objects or entities rather than a search based on keywords. Semantic search engines are needed because they better understand user queries that are usually 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, extracting, and transforming data, and representing vast volumes of unstructured, multidimensional data. Increased efficiency and accuracy of these systems are important. But the complexity associated with a large volume of data that must be processed in real time poses challenges for ML-based approaches, including data dimensionality, scalability, distributed computing, and scalability. Effective management of sparse, unbalanced, and large datasets is complex.
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 unused. More importantly, these types of solutions help businesses maximize productivity while improving the customer experience. In our next article, we'll explain how NLP could help address a number of challenges in the industrial world. So stay tuned...
