| Popis: |
We address large-scale multilingual multi-word entity (MWEntity) recognition and variant matching. Firstly, we recognise MWEntities in 22 different languages, iden- tify monolingual variant spellings and link equivalent groups of variants across all languages. We then use the previously recognised MWEntities to learn new recog- nition rules based on distributional patterns. Not requiring any linguistic tools, the method is suitable for our highly multilingual environment. When adding the new rules to the original rule-based NER system, F1 performance for Spanish increases from 42.4% to 50% (18% increase) and for English from 43.4% to 44.5% (2.5% in- crease). Besides aiming at turning free text into semi-structured data for search and for machine-processing purposes, we use the system to link related news over time and across languages, as well as to detect trends. |
