Although recent named entity (NE) annotation efforts involve the markup of nested entities, there has been limited focus on recognising such nested structures. This paper introduces and compares three techniques for modelling and recognising nested entities by means of a conventional sequence tagger. The methods are tested and evaluated on two biomedical data sets that contain entity nesting. All methods yield an improvement over the baseline tagger that is only trained on flat annotation.
|Title of host publication||Proceedings of BioNLP 2007|
|Publisher||ASSOC COMPUTATIONAL LINGUISTICS-ACL|
|Number of pages||8|
|Publication status||Published - 2007|
- txm, ttt2,