Using Conditional Probability for Discovering Semantic Relationships between Named Entities in Cultural Heritage Data
DOI:
https://doi.org/10.55630/dipp.2021.11.7Keywords:
Word Embedding, Conditional Probability, Cultural Heritage, Word Vector, Semantic RelationshipAbstract
This paper introduces a method for extracting information from various cultural heritage data sources using word embedding with feature extraction from the sentence and structured representation of the sentence. The focus is on discovering the named entities’ relations including part of speech tags and positons tags by the means of conditional probability.References
Adnan, K., & Akbar, R. (2019, October 17). An analytical study of information extraction from unstructured and multidimensional big data. Journal of Big Data 6 , 7-10.
Culture: Definition of the cultural heritage . (n.d.). Retrieved 06 01, 2021, from https://en.unesco.org/
Hedge, M., & Taludar , P. (2015). An Entity-centric Approach for Overcoming Knowledge Graph Sparsity. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 530–535). Lisbon: Association for Computational Linguistics.
Ling, W., Dyer, C., Black, A. W., & Trancoso , I. (2015). Two/Too Simple Adaptations of Word2Vec for Syntax Problems. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1299–1304). Denver: Association for Computational Linguistics.
Milkov, T., Zweig, G., & Yih, W.-t. (2013). Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 746–751). Atlanta: Association for Computational Linguistics.
Nivre, J. (2004). Incrementality in Deterministic Dependency Parsing. Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together (pp. 50-57). Barcelona: Association for Computational Linguistics.
Paneva-Marinova, D., Stoikov, J., Pavlova , L., & Nikolova , A. (2020). Data Discovery and Distributed Representation for Better Cultural Heritage Observation and Learning. Proceedings of 13th annual International Conference of Education, Research and Innovation (pp. 5696-5699). IATED: ISBN: 978-84-09-24232- 0,ISSN: 2340-1095. Retrieved from https://library.iated.org/view/PANEVAMARINOVA2020DAT
Rong, X. (2016). word2vec Parameter Explained . Retrieved from https://arxiv.org/abs/1411.2738
Simov, K. (2019). Integrated Language and Knowledge Resources for a Bulgarian- Centric Knowledge Graph. Digital Presentation and Preservation of Cultural and Scientific Heritage. Vol. 9 (pp. 65-74). Sofia: ISSN 1314-4006 (Print), eISSN 2535- 0366 (Online).
Soni, A., Shavlik , V., Shavlik, J., & Natarajan, S. (2016). Learning Relational Dependency Networks For Relation Extraction. Inductive Logic Programming (pp. 81-93). New York: Springer.
Xia, Y., & Liu, Y. (2016). Chinese Event Extraction Using DeepNeural Network with Word Embedding . Retrieved from https://arxiv.org/abs/1610.00842
