Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of a...
We present an ecient hybrid method for aligning sentences with their translations in a parallel bilingual corpus. The new algorithm is composed of a length-based and anchor matchi...
Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing...
Siddhartha Jonnalagadda, Robert Leaman, Trevor Coh...
Terminologies and other knowledge resources are widely used to aid entity recognition in specialist domain texts. As well as providing lexicons of specialist terms, linkage from t...
Angus Roberts, Robert Gaizasukas, Mark Hepple, Yik...
Domain specific entity recognition often relies on domain-specific knowledge to improve system performance. However, such knowledge often suffers from limited domain portability a...
To investigate the problem of entity recognition, we deal with the creation of the so-called Entity Name System (ENS) which is an open, public back-bone infrastructure for the (Sem...
Biomedical literature is an important source of information for chemical compounds. However, different representations and nomenclatures for chemical entities exist, which makes th...
Tiago Grego, Piotr Pezik, Francisco M. Couto, Diet...
Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respe...
Wei Wang 0011, Chuan Xiao, Xuemin Lin, Chengqi Zha...