| { | |
| "paper_id": "M95-1004", | |
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| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:12:47.646824Z" | |
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| "title": "STATISTICAL SIGNIFICANCE OF MUC-6 RESULT S", | |
| "authors": [ | |
| { | |
| "first": "Nancy", | |
| "middle": [], | |
| "last": "Chinchor", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| } | |
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| "year": "", | |
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| "abstract": "", | |
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| "abstract": [], | |
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| "text": "The results of the MUC-6 evaluation must be analyzed to determine whether close scores significantl y distinguish systems or whether the differences in those scores are a matter of chance. In order to do such an analysis , a method of computer intensive hypothesis testing was developed by SAIC for the MUC-3 results and has been use d for distinguishing MUC scores since that time . The implementation of this method for the MUC evaluations was firs t described in [1] and later the concepts behind the statistical model were explained in a more understandable manne r in [2] . This paper gives the results of the statistical testing for the three MUC-6 tasks where a single metric could b e associated with a system's performance .", | |
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| { | |
| "start": 466, | |
| "end": 469, | |
| "text": "[1]", | |
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| { | |
| "start": 573, | |
| "end": 576, | |
| "text": "[2]", | |
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| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
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| "text": "The general method employed to analyze the MUC-6 results is the Approximate Randomization method described in [3] . It is a computer intensive method which approximates the entire sample space in such a way as t o allow us to determine the significance of the differences in F-Measures between each pair of systems and th e confidence in that significance . The general method was applied on the basis of a message-by-message shuffling of a pair of MUC systems' responses to rule out differences that could have occurred by chance and to give us a picture o f the similarities of the systems in terms of performance .", | |
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| { | |
| "start": 110, | |
| "end": 113, | |
| "text": "[3]", | |
| "ref_id": "BIBREF2" | |
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| "section": "Method", | |
| "sec_num": null | |
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| { | |
| "text": "The method sorts systems into like and unlike categories . The results are shown in the following three table s for Named Entity, Template Element, and Scenario Template . These three all use the F-Measure as the single measur e for systems as defined in [4] and in the MUC-6 Test Scores appendix to this proceedings . The parameters in the F -Measure used are such that recall and precision scores are combined with equal weighting . Note that Coreference was not characterized by F or any other unified measure because of the linkages that were being evaluated . Of course, an F-Measure is calculable, but more research is necessary before we can conclude that it will combine recall an d precision in a way that is meaningful for these evaluations .", | |
| "cite_spans": [ | |
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| "start": 255, | |
| "end": 258, | |
| "text": "[4]", | |
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| "section": "Method", | |
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| "text": "The statistical results reported here are based on the strictest cutoff point for significance level (0 .01) an d high confidence in the assigned level (at least 99%) . What this method does not tell us is a numerical range withi n which F is not a significant distinguisher (such as plus or minus 3%) . Instead it provides lists of similar systems . We have to be careful to not confuse the numerical order of the F-Measures with a ranking of systems and to instead loo k at the groupings on these charts . If a group or a single system is off by itself, then that group or single system i s significantly different from its non-members . However, if there is overlap (and there is a lot of it in these results), the n the ranking of the grouped systems is impossible. In addition, two similarly acting systems could use very differen t approaches to data extraction, so there may be some other value that distinguishes these systems that has not been measured in MUC-6 .", | |
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| "section": "Method", | |
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| "text": "To prevent human error, the entire process of doing the statistical analysis is automated . An awk program extracts tallies that appear in the score report output by the scoring software and puts them in a file to be fed to the C program for approximate randomization . The C program re-calculates F-measure, recall, and precision from raw tallies for higher accuracy than during the approximate randomization comparisons . The scoring program is slow i n emacslisp and would be slowed further by calculations with higher accuracy . The statistical program outputs th e significance and confidence levels in a matrix format for the analyst to inspect . Although 10,000 shuffles are carried out, the C program is fast . Results are depicted in lists of systems that are all equivalent, i .e ., the differences in thei r scores were due to chance .", | |
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| "section": "Processing", | |
| "sec_num": null | |
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| "text": "The results are reported in a tabular format . The row headings contain the F-Measures for the systems an d the rows are ordered from highest to lowest F. The columns are ordered in the same way as the rows and the header s contain the numerical order of the F values rather than the F value itself because of the size of the table on the page .", | |
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| "section": "Results", | |
| "sec_num": null | |
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| "text": "To use the table, you first determine which system you are interested in and identify its F-Measure in the left column, then look across the row or down the corresponding column to see which systems' F-Measures its F-Measure is not significantly different from . The systems that make up that group can be considered to have gotte n their different F-Measures just by chance .", | |
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| "section": "Results", | |
| "sec_num": null | |
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| "text": "You can see, for instance, that among the Named Entity systems, the two lowest scoring systems ar e significantly different from each other and all of the all of the other systems . The two systems above them form a group which are significantly different from the other systems, but not from each other . A similar case appears i n Template Element at the low and high end of the scores . However, the important thing to note is that there is a larg e amount of overlap otherwise . The Scenario Template test shows even more overlap than the other two tasks .", | |
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| "section": "Results", | |
| "sec_num": null | |
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| "text": "The groupings in these tables allow an ordering that is less clean than we would like, but that is realistic a t this point in the evaluation methodology research . In addition to looking at the scores, evaluation research on a mor e granular level is needed to understand the differences in the systems' performance . Such research could revea l strengths and weaknesses in extracting certain information and lead to test designs that focus research in areas tha t will directly impact operational value . Also, other factors that are of interest to consumers, such as speed , development data requirements, and so on, need to be considered when making comprehensive comparisons o f systems .", | |
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| "section": "CONCLUSION S", | |
| "sec_num": null | |
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| "text": "The entire community would benefit from more refined measured values and a better understanding of ho w the differences in human performance influence the results . Distinguishing systems at such a strict cutoff as we use i n the statistics may only be justified if variations in human performance are smaller . After all, it is the human interpretation of the task definitions that informs the systems during development . Especially in Named Entity where machine performance and human performance are close, we would expect to see inherent human differences i n interpreting language during both system and answer key development to be a considerable factor holding th e machines back .", | |
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| "section": "CONCLUSION S", | |
| "sec_num": null | |
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| ], | |
| "back_matter": [ | |
| { | |
| "text": "Similar4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 ", | |
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| "section": "NE Statistical Results", | |
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| "text": "48.14 3 3 6/ 3 3 3 3 ", | |
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