Author:
Yonas Demeke Woldemariam
Affiliation:
Dept. Computing Science, Umeå University, Sweden
Keyword(s):
Answerer Performance Estimation, Syntactic-semantic based Algorithm, Answer Quality Assessments.
Abstract:
In this study, a multi-components algorithm is developed for estimating answerer performance, largely from a syntactic representation of answer content. The resulting algorithm has been integrated into semantic based answer quality prediction models, and appears to significantly improve all testsets’ baseline results, in the best case scenario. Upto 86% accuracy and 84% F-measure are scored by these models. Also, answer quality classifiers yeild upto 100% recall and 98% precision. Following the transformation of joint syntactic-punctuation information into the identified expertise dimensions (e.g., authoritativeness, analytical, descriptiveness, completeness) that formally define answerer performance, extensive algorithm analyses have been carried on almost 142,246 answers extracted from diverse sets of 13 different QA forums. The analyses prove that incorporating competence information into answer quality models certainly leads to nearly perfect models. Moreover, we found out that t
he syntactic based algorithm with semantic based models yield better results than answer quality prediction modles built on shallow linguistic or meta-features presented in related works.
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