By translating job descriptions, training content or even the CVs of their candidates into skills, organizations can link all of their HR processes together, enabling them to take more relevant actions. Natural Language Processing (NLP) plays an essential role in optimizing the analysis and management of this HR content. In this article, we take a closer look at the main NLP techniques used in this context.
NLP (Natural Language Processing) is a branch of artificial intelligence that focuses on human language. Combining linguistics, computer science and AI, NLP aims to make written or spoken language understandable to machines. It relies on syntactic and semantic analysis algorithms allowing machines to understand human language, handle it and generate it.
→ Good to know:
Syntactic analysis sheds light on the structure of a text, while semantic analysis provides the meaning of a sentence in a context.
How does Natural Language Processing work?
→ Natural language pre-processing methods
A pre-processing phase of information to be analyzed is often necessary before using NLP techniques. This preparatory stage allows the raw content (the text of the job description, the training description or the candidate’s CV) to be transformed into data that computers can use.
The methods used for this preparatory work vary according to the nature of the data provided. Sometimes it is necessary to separate the words of a sentence into several parts called “tokens”, to keep only the root of the words (stemming), or remove punctuation and numbers.
→ Natural language processing methods
There are three main approaches to natural language processing: rule-based methods, machine learning techniques and deep learning techniques.
1. Rule-based methods
These methods consist of establishing rules according to the data exploited and the purpose of the analysis. They can be used to solve simple problems such as extracting structured data from unstructured data. For example, rule-based methods can identify the parts of a resume related to a candidate’s education or work experience based on keywords.
2. Machine learning techniques
Machine learning is a branch of artificial intelligence that allows machines to learn from data with little or no human intervention. There are two types of machine learning:
- supervised machine learning:
In supervised machine learning, the result to be obtained is already known, and the goal is to teach the algorithm to associate new data with previously identified data. This type of machine learning allows us to associate a new job title with a job already identified.
- unsupervised machine learning:
With unsupervised machine learning, we do not yet know the result we will obtain. This version of machine learning uses, for example, clustering, which groups data into categories without knowing what the categories correspond to.
Machine learning techniques need a large amount of data to perform well., and the more data provided to the algorithm, the better it gets. However, one of the limitations of machine learning is that it depends on the quality and quantity of the training data. Moreover, most of these techniques eventually stop improving with the volume of data, which is not the case with deep learning techniques.
3. Deep learning techniques
Deep learning is a subcategory of machine learning that uses artificial neural networks to simulate brain function. Like the neural networks in the brain, artificial neural networks are structured in several successive layers of neurons., and the greater the number of layers, the deeper the network.
Deep learning techniques do not require identifying variables to be searched for in the data in advance, as the algorithm can identify them by itself. These techniques are particularly used in the analysis of unstructured data, such as textual data. The best results currently obtained in NLP are thanks to word embedding techniques, which use complex artificial neural networks.
Within human resources, deep learning techniques are used to extract skills from training content, CVs or job descriptions.
Deep learning models require more significant amounts of data to learn than machine learning, but unlike machine learning, they continue to improve with new data. Furthermore, one of the main limitations of deep learning is the computational power required by neural networks.
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