Neural network based word embeddings have demonstrated outstanding results in a variety of tasks, and become a standard input for Natural Language Processing (NLP) related deep learning methods. Despite these representations are able to capture semantic regularities in languages, some general questions, e.g., "what kinds of semantic relations do the embeddings represent?" and "how could the semantic relations be retrieved from an embedding?" are not clear and very little relevant work has been done. In this study, we propose a new approach to exploring the semantic relations represented in neural embeddings based on WordNet and Unified Medical Language System (UMLS). Our study demonstrates that neural embeddings do prefer some semantic relations and that the neural embeddings also represent diverse semantic relations. Our study also finds that the Named Entity Recognition (NER)-based phrase composition outperforms Word2phrase and the word variants do not affect the performance on analogy and semantic relation tasks. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2017. / July 17, 2017. / semantic relation, word2vec, word embedding, WordNet / Includes bibliographical references. / Xiuwen Liu, Professor Directing Thesis; Zhe He, Committee Member; Peixiang Zhao, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_552045 |
| Contributors | Chen, Zhiwei (authoraut), Liu, Xiuwen, 1966- (professor directing thesis), He, Zhe (Professor of Information Studies) (committee member), Zhao, Peixiang (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Computer Science (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, master thesis |
| Format | 1 online resource (59 pages), computer, application/pdf |
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