| N13-1063 | direct usage of same embeddings on | English NER | . In this section we conducted |
| P02-1060 | successfully trained and applied in | English NER | . To our knowledge , our system |
| D12-1120 | tasks : Chinese POS tagging and | English NER | . In both cases , we use a domain |
| N09-1032 | loss in the domain transfer for | English NER | without any labeled target domain |
| J14-2008 | Capitalization is a key feature of an | English NER | . Arabic is at a disadvantage |
| N13-1063 | time for tagging one sentence in | English NER | was reduced from 5.6 ms to 1.6 |
| I05-3006 | Pierre 2002 -RSB- developed an | English NER | system capable of identifying |
| N04-4010 | considerable amount of work on | English NER | yielding good performance ( Tjong |
| D08-1030 | language independent approaches to | English NER | are systems that employ Maximum |
| N13-1006 | effectiveness of word clustering for | English NER | has been proved in previous work |
| D15-1064 | . Features include many common | English NER | features , e.g. character unigrams |
| P06-1028 | target chunk ( segment ) . The | English NER | data was taken from the Reuters |
| D15-1104 | performance on the CoNLL ’03 | English NER | data set . Recent works on NER |
| N12-1052 | results for wordbased SSL for | English NER | . As an alternative to clustering |
| J13-2001 | more difficult problem for this | English NER | system . 4.2 The Baseline System |
| D11-1143 | , we reannotate the CoNLL-2003 | English NER | statistical classifiers behave |
| J13-2001 | include alignment , and both the | English NER | model and the alignment model |
| N12-1052 | the state-of-the-art result for | English NER | . 3 Monolingual Cluster Experiments |
| N13-1006 | performance of both Chinese and | English NER | systems improves with decreasing |
| N04-4010 | & King ( 2003 ) carried out | English NER | on word lattices . We are interested |
