Abstract
We introduce a Language-consistent multi-lingual Open Relation Extraction Model (LOREM) for finding relation tuples of any type between entities in unstructured texts. LOREM does not rely on language-specific knowledge or external NLP tools such as translators or PoS-taggers, and exploits information and structures that are consistent over different languages. This allows our model to be easily extended with only limited training efforts to new languages, but also provides a boost to performance for a given single language. An extensive evaluation performed on 5 languages shows that LOREM outperforms state-of-the-art mono-lingual and cross-lingual open relation extractors. Moreover, experiments on languages with no or only little training data indicate that LOREM generalizes to other languages than the languages that it is trained on.
Original language | English |
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Title of host publication | Proceedings of the The Web Conference (WWW) |
Place of Publication | Taipei, Taiwan |
Pages | 1830-1838 |
Number of pages | 9 |
ISBN (Electronic) | 978-1-4503-7023-3 |
DOIs | |
Publication status | Published - 20 Apr 2020 |
Event | IW3C2: The Web Conference 2020 - Taipei, Taiwan Duration: 20 Apr 2020 → 24 Apr 2020 https://www.iw3c2.org/ |
Conference
Conference | IW3C2: The Web Conference 2020 |
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Country/Territory | Taiwan |
City | Taipei |
Period | 20/04/20 → 24/04/20 |
Internet address |
Keywords
- open domain relation extraction
- multi-lingual relation extraction
- text mining