Name
Title | ||
|---|---|---|
Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval |
Abstract | ||
|---|---|---|
The Q&A community has become an important way for people to access knowledge and information from the Internet. However, existing translation based models do not consider term weights when assigning weights to query terms in question retrieval. We improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a community-based question retrieval method that combines question and answer on quality and question relevance (T
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
LM+). We have also proposed a question retrieval method based on convolutional neural networks. The results show that compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91 % and 6.31%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BESC48373.2019.8963362 | 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC) |
Keywords | Field | DocType |
Question retrieval,Translation model,Topic model,Learn to rank,Convolutional neural network | Weighting,Convolutional neural network,Computer science,Artificial intelligence,Topic model,Machine learning,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-7281-4763-5 | 0 | 0.34 |
References | Authors | |
0 | 3 | |