.png){kind=icon}
返回
Query Intent Recognition Based on Multi-Class Features
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2869585.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In order to enhance the user search experience of the search engine, an intent recognition search based on natural language input is proposed. By using reality mining technology to obtain the potential consciousness information from the query expression, search engines can better predict the query results that meet users' requirements. With the development of conventional machine learning and deep learning, it is possible to further improve the accuracy of prediction results. This paper adopts a similarity calculation method based on long short-term memory (LSTM) and a traditional machine learning method based on multi-feature extraction. It is found that entity features can significantly improve the accuracy of intention classification. Second, the accuracy of intention classification based on the feature sequence constructed by key entities is up to 94.16% in the field of manual labeling by using the BiLSTM classification model.
Keyword:
Intent recognition
multi-class
long short term memory (LSTM)
reality mining
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏分享收藏
Classifying the user intent of User intent of web queries web queries using k-means clustering.png)
INTERNET RESEARCH
INTERNET RESEARCHIF6.8
PREAI
分享收藏A dynamic programming approach to missing data estimation using neural networks使用神经网络进行缺失数据估计的动态规划方法
PREAI
分享收藏
查看更多内容