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| Content Provider | ACM Digital Library |
|---|---|
| Author | Carterette, Ben Webber, William Chandar, Praveen |
| Abstract | The notion of relevance differs between assessors, thus giving rise to assessor disagreement. Although assessor disagreement has been frequently observed, the factors leading to disagreement are still an open problem. In this paper we study the relationship between assessor disagreement and various topic independent factors such as readability and cohesiveness. We build a logistic model using reading level and other simple document features to predict assessor disagreement and rank documents by decreasing probability of disagreement. We compare the predictive power of these document-level features with that of a meta-search feature that aggregates a document's ranking across multiple retrieval runs. Our features are shown to be on a par with the meta-search feature, without requiring a large and diverse set of retrieval runs to calculate. Surprisingly, however, we find that the reading level features are negatively correlated with disagreement, suggesting that they are detecting some other aspect of document content. |
| Starting Page | 745 |
| Ending Page | 748 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450320344 |
| DOI | 10.1145/2484028.2484161 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-07-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Evaluation Retrieval experiment |
| Content Type | Text |
| Resource Type | Article |
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