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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-18493 |
| Date | January 2019 |
| Creators | Marakani, Sumeesha |
| Publisher | Blekinge Tekniska Högskola, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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