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Answer ranking in community Q/A websites using Natural Language Processing
Abstract
Community blogs and Q/A websites have always been very helpful for the vast internet community in providing ideas, answers and suggestions to their various diversified questions, they range from technical, social, political, education etc. hence, providing the best results for the questions posed in the sites makes the lives of the people better and more efficient. This paper focusses on providing a new method for ranking the best answer for any given question posed in the community Q/A websites which is different from the traditional ranking based on sole point of ranking only with the help of number of votes that each answer obtains. Here in this paper we presented a new ranking method based on reviews/comments on the answer posts.
Keywords: Machine learning, Natural language processing.
We propose an automatic approach that, besides the quality, also considers the answer’s recency to generating the ranking. We have used textual and non-textual features that indicate the answers’ quality and recency, extracted from the users’ answers in the CQA environment as a whole. In our approach, quality is used to classify the answers between good and poor, using a threshold value, generating two sets of answers: high quality and low quality. Then, both sets are sorted into recency order. Finally, these sets are concatenated, giving rise to the final ranking, so that the best and most current answers are in the top positions. To verify our proposal’s effectiveness, we have performed a case study in Stack Overflow CQA with a set of experiments, using different combinations of characteristics and different learning to rank Stack Overflow. Then, our main contributions are: (1) an approach to ranking answers of a questions dataset on the recency and quality of an answer; (2) a thorough evaluation of 9 learning to rank algorithms, showing that Coordinate Ascent and LambdaMart have the best performance in this task; (3) a feature analysis, which has shown that features related to the age of the response contributed to improving the ranking performance taking recency and quality into account. Furthermore, as far as we know, it is the first work that considers the recency of an answer in this task.
We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations.
The effectiveness of various forms of contextual information in a postprocessing system for detection and correction of errors in words is examined. Various algorithms utilizing context are considered, from a dictionary algorithm which has available the maximum amount of information, to a set of contextual algorithms utilizing positional binary n-gram statistics. The latter information differs from the usual n-gram letter statistics in that the probabilities are position-dependent and each is quantized to 1 or 0, depending upon whether or not it is nonzero. This type of information is extremely compact and the computation for error correction is orders of magnitude less than that required by the dictionary algorithm.
API design for machine learning software: experiences from the scikit-learn project
- Lars Bentinck
Lars Bentinck et al. "API design for machine learning
software: experiences from the scikit-learn project". In:
ECML PKDD Workshop: Languages for Data Mining and
Machine Learning.
A Decision Tree Method for Finding and Classifying Names in Texts
- S Sekine
- R Grishman
- H Shinnou
Sekine, S., Grishman, R., and Shinnou, H., A Decision Tree
Method for Finding and Classifying Names in Texts, In
Proceedings of the Sixth Workshop on Very Large Corpora,
171-178, Montreal, Canada, August, 1998.