Effects of Semantic Network Structure of English on Word Processing

• Thesis title in Czech: Efekty struktury sémantické sítě angličtiny na zpracování slov
• Thesis title in English: Effects of Semantic Network Structure of English on Word Processing
• Key words: sémantická síť|zpracování slov|slovní vektory|strojové učení|psycholingvistika|network science
• English key words: semantic network|word processing|word vectors|machine learning|psycholinguistics|network science
• Academic year of topic announcement: 2023/2024
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Department of the English Language and ELT Methodology (21-UAJD)
• Supervisor: doc. Dr. phil. Eva Maria Luef, Mag. phil.
• Author: hidden - assigned and confirmed by the Study Dept.
• Date of registration: 10.10.2023
• Date of assignment: 10.10.2023
• Administrator's approval: approved
• Confirmed by Study dept. on: 12.08.2024
• Date and time of defence: 09.09.2024 08:30
• Date of electronic submission: 12.08.2024
• Date of proceeded defence: 09.09.2024
• Submitted/finalized: committed by student and finalized
• Opponents: Mgr. Kateřina Vašků, Ph.D.

Guidelines

This work analyses semantic network of English using the tools of social network analysis (SNA). Assuming small-world characteristics of this network, the focus will be on the comparison of words from lexical islands, i.e., smaller interconnected clusters within the network and the giant component, i.e., the biggest interconnected chunk within the network. It will build upon previous research on phonological networks that showed that words from lexical islands are being processed more quickly and efficiently during psycholinguistic experiments. The aim of this work is to test if similar effect occurs in semantic network. The semantic network in this work is constructed by first using a machine learning method based on word vector embeddings (e.g., word2vec, BERT, etc.) on a corpus of text that capture the meaning of a word as set of vectors in vector space. The embeddings can be visualized as data points in space where each data point represents one word and the distance between them represents the semantic similarity as calculated from the corpus by the algorithm. To construct an interconnected semantic network, a maximal distance between two data points (i.e., words) will be established, which produces a list of edges between the words of the network.
Práce bude vypracována v anglickém jazyce.

References

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