Print Email Facebook Twitter Language-consistent Open Relation Extraction Title Language-consistent Open Relation Extraction: from Multilingual Text Corpora Author Harting, Tom (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Lofi, Christoph (mentor) Houben, Geert-Jan (graduation committee) Brinkman, Willem-Paul (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2019-07-12 Abstract Open Relation Extraction (ORE) aims to find arbitrary relation tuples between entities in unstructured texts. Even though recent research efforts yield state-of-the-art results for the ORE task by utilizing neural network based models, these works are solely focused on the English language. Methods were proposed to tackle the ORE task for multiple languages, yet these works fail to exploit relation patterns that are consistent over languages. Moreover, they require additional data to train translators, hindering efficient extension to new languages. In this work, we introduce a Language-consistent Open Relation Extraction Model (LOREM). By adding a language-consistent component to the current state-of-the-art open relation extraction model, we enable exploitation of information from multiple languages. Since we remove all dependencies on language-specific knowledge and external NLP tools such as translators, it is relatively easy to extend our model to new languages. An extensive evaluation performed on 5 languages shows that LOREM outperforms state-of-the-art monolingual and cross-lingual open relation extractors. Moreover, experiments on low- and even no-resource languages indicate that LOREM generalizes to other languages than the languages that it is trained on. Subject Open Relation ExtractionNatural Language ProcessingInformation Extraction To reference this document use: http://resolver.tudelft.nl/uuid:5c713c07-fbdd-4ad9-8914-d6e64374d526 Part of collection Student theses Document type master thesis Rights © 2019 Tom Harting Files PDF _Final_online_Thesis_LOREM.pdf 2.78 MB Close viewer /islandora/object/uuid:5c713c07-fbdd-4ad9-8914-d6e64374d526/datastream/OBJ/view