Record linkage methods evaluate the disclosure risk of revealing confidential information in anonymized datasets that are publicly distributed. Concretely, they measure the capacity of an intruder to link records in the original dataset with those in the masked one. In the past, masking and record linkage methods have been developed focused on numerical or ordinal data. Recently, motivated by the proliferation of textual information, some authors have proposed masking methods to anonymize textual data. Textual attributes should be interpreted according to their semantics, which makes them more difficult to manage and compare than numerical data. In this paper, the authors propose a new record linkage method specially tailored to accurately evaluate their disclosure risk.