Semantic Analysis and Search

Improve the match between job announcements and candidates with semantic analysis and search tools for recruitment.

Semantic analysis in the human resources sector

Following the introduction of intelligent systems in the recruitment process, semantic search is increasingly used in the HR sector. When the number of applications is very high, recruiters need to speed up the CV screening process and other routine tasks in order to dedicate themselves to the identification and selection of the best talent.

 

Having a semantic search engine available supports the HR team in carrying out their tasks on a daily basis without the risk of compromising the quality of the results. By carrying out a search within the CV database, the semantic algorithms identify both CVs containing the keywords entered by the recruiter, and those that have words with a similar meaning, therefore also intercepting candidates who have not entered the specific search key in their CV.

What is the Inda semantic search engine process

Inda’s semantic search allows the analysis of the semantic meaning of words and text in Italian and English. By working on keywords and researching and understanding their synonyms, Inda is able to enhance the semantic search of candidates. Inda creates a rank of the candidates with a semantic similarity score, identifying the best candidates for a job description by comparing them with other similar profiles.

  • Semantic search
  • Semantic scoring
  • Semantic matching

 

Search candidates

 

Search by semantic keywords 

Through Deep Learning algorithms and the creation of Word Embedding and Document Embedding, it is possible to do a search of CVs based on specific keywords. Going beyond the limits of traditional search, recruiters can carry out targeted searches even in areas outside of their expertise, and at the same time broaden their vocabulary. The final result is a shortlist of CVs with content that is semantically close to the keyword, even in the absence of a direct match.

Attribution of a score 

Based on the research and analysis of the specific subjects, a semantic similarity score is assigned to each CV to evaluate how closely it corresponds to the search: from 100% (perfect matching) to 0% (extremely distant semantic fields). Assigning a relevance score to each CV means that the CVs resulting from the search can be sorted and filed for future use.

Matching between job announcements and candidates  Starting with the job announcement, the Document Embedding operation makes the matching between the Job description and the candidate profiles much more practical. By searching for similar candidates, Inda is able to refine the search results by identifying CVs that are semantically similar to the sample profile with the aim of selecting the best candidates.

Do you want to improve your recruitment?

Request a demo of Inda to see how semantic analysis and research techniques can lead to selecting the best talent

How does semantic CV analysis and search work

The artificial intelligence that powers Inda facilitates improved recruitment. Natural Language Processing and Semantic Search techniques are specifically used in the semantic analysis and search processes.

This is a Natural Language Processing (NLP) technique that allows you to create a map associating each word (“monogram", a single word, or an "n-gram", a sequence of a number of words) to a place in a multidimensional space. The map is created so that words with similar semantic meanings are mapped to mathematically close points.

Based on the mapping between words and vectors, it is possible to build a map of text documents, such as CVs and job descriptions, in the same vector space.

The maps described above allow you to calculate the semantic proximity between different words, between a word and a text or between two texts, tracing all these operations to mathematical calculations between the corresponding vectors.

The advantages of semantic analysis and search for recruitment

Facilitates the search for candidates in the CV database
Reduces pre-screening time
Improves the selection of talent
Increases the productivity of the HR team

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