Nancy Chinchor. Statistical Significance of MUC-7 Results. MUC-7. Fairfax, Virginia. 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....text. Moreover, HMM seems more and more used in NE recognition because of the efficiency of the Viterbi algorithm [Viterbi67] used in decoding the NE class state sequence. However, the performance of a machine learning system is always poorer than that of a rule based one by about 2 [Chinchor95b] [Chinchor98b]. This may be because current machine learning approaches capture important evidence behind NER problem much less effectively than human experts who handcraft the rules, although machine learning approaches always provide important statistical information that is not available to human experts. ....

Nancy Chinchor. Statistical Significance of MUC-7 Results. MUC-7. Fairfax, Virginia. 1998.

....names in text. Moreover, HMM seems more and more used in NE recognition because of the efficiency of the Viterbi algorithm [Viterbi67] used in decoding the NE class state sequence. However, the performance of a machine learning system is always poorer than that of a rule based one by about 2 [Chinchor95b] [Chinchor98b] This may be because current machine learning approaches capture important evidence behind NER problem much less effectively than human experts who handcraft the rules, although machine learning approaches always provide important statistical information that is not available to ....

Nancy Chinchor. Statistical Significance of MUC-6 Results. MUC-6. Columbia, Maryland. 1995.

....of capturing the locality of phenomena, which indicates names in text, and the efficient Viterbi algorithm [Viterbi67] used in decoding the NE class state sequence. However, the performance of a machine learning system is always poorer than that of a rule based one by about 2 [Chinchor95b] [Chinchor98b]. This may be because current machine learning approaches capture important evidence behind NER problem much less effectively than human experts who handcraft the rules, although machine learning approaches always provide important statistical learned information that is not available to human ....

Nancy Chinchor. Statistical Significance of MUC-7 Results. MUC-7. Fairfax, Virginia. 1998.

....of its ability of capturing the locality of phenomena, which indicates names in text, and the efficient Viterbi algorithm [Viterbi67] used in decoding the NE class state sequence. However, the performance of a machine learning system is always poorer than that of a rule based one by about 2 [Chinchor95b] [Chinchor98b] This may be because current machine learning approaches capture important evidence behind NER problem much less effectively than human experts who handcraft the rules, although machine learning approaches always provide important statistical learned information that is not ....

Nancy Chinchor. Statistical Significance of MUC-6 Results. MUC-6. Columbia, Maryland. 1995.

....of capturing the locality of phenomena, which indicates names in text, and the efficient Viterbi algorithm [Viterbi67] used in decoding the NE class state sequence. However, the performance of a machine learning system is always poorer than that of a rule based one by about 2 [Chinchor95b] [Chinchor98b]. This may be because current machine learning approaches capture important evidence behind NER problem much less effectively than human experts who handcraft the rules, although machinelearning approaches always provide important statistical learned information that is not available to human ....

Nancy Chinchor. Statistical Significance of MUC-7 Results. MUC-7. Fairfax, Virginia. 1998.

....of its ability of capturing the locality of phenomena, which indicates names in text, and the efficient Viterbi algorithm [Viterbi67] used in decoding the NE class state sequence. However, the performance of a machine learning system is always poorer than that of a rule based one by about 2 [Chinchor95b] [Chinchor98b] This may be because current machine learning approaches capture important evidence behind NER problem much less effectively than human experts who handcraft the rules, although machinelearning approaches always provide important statistical learned information that is not available ....

Nancy Chinchor. Statistical Significance of MUC-6 Results. MUC-6. Columbia, Maryland. 1995.

.... to as TE, or Template Element, tasks have become extremely accurate (over 95 accuracy for the best systems) In interpreting MUC figures, it should also be borne in mind that the overall recall and precision of human provided IE information as a whole is estimated to be about 20 worse [16] [14], 15] than the best human performance; it was measured by how well intelligence analysts perform the task manually when compared to a gold star experienced intelligence analyst. Adaptivity in the MUC development context has meant the one month period in which competing centres adapt their ....

N. Chinchor. The statistical significance of the MUC-5 results. In Proceedings of the Fifth Message Understanding Conference (MUC-5), pages 79--83. Morgan Kaufmann, 1993.

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CHINCHOR, N. The Statistical Significance of the MUC-5 Results. In: Procedings of the Fifth Message Understanding Conference (MUC-5) . Morgan Kaufmann, 1993, 79-83.

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