| Experimental data | |CoNLL trn|CoNLL tst|IEER|KDD-D|KDD-T |
| Experimental data | Table 2: Percentages of NEs in CoNLL , IEER, and KDD. |
| Experimental data | As training data for all models evaluated we used the CoNLL 2003 English NER dataset, a corpus of approximately 300,000 tokens of Reuters news from 1992 annotated with person, location, organization and miscellaneous NE labels (Sang and Meulder, 2003). |
| Experimental setup | We use BIO encoding as in the original CoNLL task (Sang and Meulder, 2003). |
| Related work | (2010) show that adapting from CoNLL to MUC-7 (Chinchor, 1998) data (thus between different newswire sources), the best unsupervised feature (Brown clusters) improves F1 from .68 to .79. |
| Abstract | Evaluation on the CoNLL 2008 benchmark dataset demonstrates that our method outperforms competitive unsupervised approaches by a wide margin. |
| Experimental Setup | Data For evaluation purposes, the system’s output was compared against the CoNLL 2008 shared task dataset (Surdeanu et al., 2008) which provides |
| Experimental Setup | Our implementation allocates up to N = 21 clusters2 for each verb, one for each of the 20 most frequent functions in the CoNLL dataset and a default cluster for all other functions. |
| Introduction | We test the effectiveness of our induction method on the CoNLL 2008 benchmark |
| Learning Setting | with the CoNLL 2008 benchmark dataset used for evaluation in our experiments. |
| Results | (The following numbers are derived from the CoNLL dataset4 in the auto/auto setting.) |