Publication details
Named Entity Linking in English-Czech Parallel Corpus
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | Text, Speech, and Dialogue, 27th International Conference, TSD 2024, Part I |
| MU Faculty or unit | |
| Citation | |
| web | https://link.springer.com/book/10.1007/978-3-031-70563-2 |
| Doi | http://dx.doi.org/10.1007/978-3-031-70563-2_12 |
| Keywords | named entity recognition; named entity linking; parallel corpus; sentence similarity |
| Description | We present a procedure to build relatively quickly new resources with annotated named entities and their linking to Wikidata. First, we applied state-of-the-art models for named entity recognition on a sentence-aligned parallel English-Czech corpus. We selected the most common entity classes: person, location, organization, and miscellaneous. Second, we manually checked the corpus in a suitably set annotation application. Third, we used a state-of-the-art tool for named entity linking and enhanced the ranking using sentence embeddings obtained by sentence transformers. We then checked manually whether the linking to knowledge bases was correct. As a result, we added two annotation layers to an existing parallel corpus: one with the named entities and one with links to Wikidata. The corpus contains 14,881 parallel Czech-English sentences and 3,769 links to Wikidata. The corpus can be used for training more robust named entity recognition and named entity linking models and for linguistic research of parallel news texts. |