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Employee Matching Using Machine Learning Methods
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Background: Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individuals.

Objectives: This master thesis is a collaboration with Volvo Cars to attempt applying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free-flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques.

Methods: Through the course of this project, various techniques are employed and experimented with to study, analyze and understand the unlabelled textual data using NLP techniques. Through the course of the project, we try to match individuals based on information extracted from these techniques using Unsupervised MachineLearning methods (K-means clustering).Results. The results obtained from applying the various NLP techniques are explained along with the algorithms that are implemented. Inferences deduced about the properties of the data and methodologies are discussed.

Conclusions: The results obtained from this project have shown that it is possible to extract patterns among people based on free-text data written about them. The future aim is to incorporate the semantic relationship between the words to be able to identify people who are similar and dissimilar based on the data they share about themselves.

Place, publisher, year, edition, pages
2019. , p. 68
Keywords [en]
Machine Learning, Unsupervised Learning, Natural Language Processing, Information Retrieval, text analysis
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-18493OAI: oai:DiVA.org:bth-18493DiVA, id: diva2:1338281
External cooperation
Volvo Cars
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Presentation
2019-05-27, J3208, Campus Gräsvik, Karlskrona, 11:00 (English)
Supervisors
Examiners
Available from: 2019-07-24 Created: 2019-07-21 Last updated: 2019-07-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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