Extreme Learning Machine for Intent Classification of Web Data

Web search engines return a large amount of results for a user search query. Understanding the intent of these search queries can help us to narrow down the search results based on the type of information needed. In the research reported in this paper, we implemented machine learning algorithms to validate the accuracy of the classification of user search query. Broad categories of web query data are used from two different sources. Feature sets extracted solely from the web query are used to train the machine learning classifier. Classification results reveal that the performance of extreme learning machine (ELM) is much better when classifying user query intent than other machine learning classifiers.