Base_JSON/prefixM/json/M93/M93-1008.json

{
    "paper_id": "M93-1008",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T03:14:20.340745Z"
    },
    "title": "THE STATISTICAL SIGNIFICANCE OF THE MUC-5 RESULT S",
    "authors": [
        {
            "first": "Nancy",
            "middle": [],
            "last": "Chinchor",
            "suffix": "",
            "affiliation": {},
            "email": "chinchor@gso.saic.com"
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "",
    "pdf_parse": {
        "paper_id": "M93-1008",
        "_pdf_hash": "",
        "abstract": [],
        "body_text": [
            {
                "text": "The statistical significance of the results of the MUC-5 evaluation is determined using a computer-intensiv e method of hypothesis testing known as approximate randomization . The exact method is described in detail in 111 an d [2] and has been used as the accepted statistical test for the MUC results since MUC-3 . The purpose of the statistica l testing is to determine whether the scores of the systems are different by chance or due to a significant difference i n the character of the systems .",
                "cite_spans": [
                    {
                        "start": 228,
                        "end": 231,
                        "text": "[2]",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "INTRODUCTION",
                "sec_num": null
            },
            {
                "text": "Statistical significance results are reported here for the following metrics : Error per Response Fill, F-Mcasure with recall and precision weighted equally, and Richness-Normalized Error (minimum and maximum) . The systems are compared for the same domain and language and, thus, there are four figures for each metric : English Join t Ventures (EJV), Japanese Joint Ventures (JJV), English Microelectronics (EME), and Japanese Microelectronics (JME) . The format of the reporting is according to the groupings of the systems which are not significantly differen t from each other at the 0.01 level with a confidence of at least 99% . Systems which are not significantly different fro m each other are underscored on the same line . The systems are numbered to save space and the correspondence of th e number and system site are given below the significance results .",
                "cite_spans": [],
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                "section": "STATISTICAL SIGNIFICANCE RESULT S",
                "sec_num": null
            },
            {
                "text": "It is interesting to note that the rankings of systems do not change when using the Error per Response Fil l metric or the F-Measure. The numerical rankings change slightly (numbers 6 and 7 in EJV reverse, and numbers 4 and 5 in JJV reverse), but those changes are not significant statistically because the two members in each of th e reversed pairs are both in the same significance grouping for both of the two metrics . It is also interesting to note tha t the Error per Response Fill metric distinguishes four more systems than the F-Measure over all domains and languages . The Richness-Normalized Error metric distinguishes far fewer systems statistically than the Error pe r Response Fill metric with 29 systems distinguished by Richness-Normalized Error as opposed to 55 by Error per Response Fill for EJV alone . Both the minimum and maximum Richness-Normalized Error metrics produce the sam e rankings and statistical results so are conflated here . The statistical groupings of systems for Richness-Normalized Error are so large and so numerous that systems cannot be distinguished well enough to reflect their perceived differences in performance . It is believed that this is due to the fact that the Richness-Normalized Error metric ignores th e amount of spurious data generated by a system and that the amount and kind of spurious data generated impacts th e perception of how well the system is doing in an operational setting .",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "STATISTICAL SIGNIFICANCE RESULT S",
                "sec_num": null
            },
            {
                "text": "The approximate randomization method has been used to determine the statistical significance of the rankings of systems for MUC-5 . It is also useful for reflecting on the relative merits of the evaluation metrics . The statistical results show that the Error per Response Fill metric is the most sensitive metric of the three in terms o f distinguishing systems. However, no statistically significant changes in ranking occur when F-Measure is used . The Richness-Normalized Error metric distinguishes far fewer systems than either of the other metrics .",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "CONCLUSIONS",
                "sec_num": null
            },
            {
                "text": "English Joint Ventures -Error per Response Fill 1 2 3 4 5 6 7 8 9 10 11 12 13 1) GE/CMU 2) BBN 3) SRI 4) UMASS/HU 5) PMAX 6) USUSSEX 7) NMSUIBR 8) NYU 9) SRA 10) PRC 11) USC 12) MITRE 13)TRW panese Microelectronics -Richness-Normalized Error 1 2 3 4 5",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "CONCLUSIONS",
                "sec_num": null
            },
            {
                "text": "EQUATION",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [
                    {
                        "start": 0,
                        "end": 8,
                        "text": "EQUATION",
                        "ref_id": "EQREF",
                        "raw_str": "I) GE/CMU-OPT 2) GFJCMU 3) NMSU/BR 4) SRA 5)",
                        "eq_num": "SRI"
                    }
                ],
                "section": "CONCLUSIONS",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "English Microelectronics -F-Measure (P&R ) 1 2 3 4 5 6 7English Joint Ventures -Richness-Normalized Erro r 1 2 3 4 5 6 7 8 9 10 11 12 132) SRI 3) NYU 4) UMASS/H U 9) MITRE I0) PRC 1 I) PMAX 5) GE/CMU 6) USUSSEX 7) NMSU/BR 12) USC 13) TRW",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "annex",
                "sec_num": null
            }
        ],
        "bib_entries": {},
        "ref_entries": {
            "FIGREF0": {
                "type_str": "figure",
                "uris": null,
                "num": null,
                "text": "Chinchor, N ., L . Hirschman, and D . Lewis (1993) \"Evaluating Message Understanding Systems : An Analysi s of the Third Message Understanding Conference (MUC-3)\" Computational Linguistics 19(3) .[2] Chinchor, N. (1992) . \"The Statistical Significance of the MUC-4 Results\" Proceedings of the Fourth Message Understanding Conference (MUC-4) . Morgan Kaufmann, Publishers . San Mateo, CA ."
            }
        }
    }
}