| W09-3302 | themselves . We present the first | NER evaluation | on a Wikipedia gold standard |
| E14-1051 | than CoNLL 2003 challengers . The | NER evaluation | task is mainly syntactic . As |
| D15-1104 | extensively used in prior work on | NER evaluation | ( Tjong Kim Sang and De Meulder |
| E09-1070 | texts affect compatibility for | NER evaluation | e.g. , the CoNLL corpus formats |
| E09-1041 | Corpus The corpus we used for | NER evaluation | is the same as the one described |
| E14-4014 | labelings are compared using the CoNLL | NER evaluation | tool ( Sang and Meulder , 2003 |
| W03-1718 | provides a standard testbed for | NER evaluation | . Recent research on English |
| W09-3302 | closer match BBN annotations . 2.2 | NER evaluation | Meaningful automatic evaluation |
| P14-5003 | prediction of the nested entities . 4.3 | NER Evaluation | For comparison with previous |
| N04-4010 | , 2003 ) 1 . In the CoNLL 2003 | NER evaluation | , 5 out of 16 systems use MaxEnt |
| P09-2089 | . For instance , in the first | NER evaluation | for Portuguese , HAREM ( San |
| P14-2076 | respectively . fM is comparable to the | NER evaluation | from the CoNLL 2003 shared task |
