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Automated Analysis of Text in Graduate School Recommendations NLP

Author(s):
Heilman, Michael; Breyer, F. Jay; Williams, Frank; Klieger, David M.; Flor, Michael
Publication Year:
2015
Report Number:
RR-15-23
Source:
ETS Research Report
Document Type:
Report
Page Count:
14
Subject/Key Words:
Letters of Recommendation, Automated Scoring and Natural Language Processing

Abstract

In this report, we develop an approach for analyzing recommendations and evaluate the approach on four tasks: (a) identifying which sentences are actually about the student, (b) measuring specificity, (c) measuring sentiment, and (d) predicting recommender ratings. We find substantial agreement with human annotations and analyze the effects of different types of features.

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