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This research explores a novel approach for web query intent classification utilizing encyclopedia knowledge. It focuses on seed term extraction, where we enhance traditional classification methods to improve performance in identifying users' intent. Given the challenges of short-text queries and limited training data, we employ semantic similarity calculations and Markov Random Walks to extend our seed term set. Our experiments, utilizing data from BaiduZhidao and Sogou, show promising results in intent classification using SVM, thereby addressing the inadequacies of classical methods in real retrieval scenarios.
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Bingquan Liu, Ming Liu, Gang Hu Harbin Institute of Technology Classification of Web Query Intent Using Encyclopedia基于百科知识的查询意图获取
Meaning Seed term extraction Intent category Experiments results Outline
Improve performance of retrieve system by searching user’s intent Classical category methods need adequate training corpus, whereas, it’s unavailable in retrieve situation. Classical category methods mostly focus on long-text, contrastingly, query is quite short-text. Meaning
Semantic similarity calculation between words based on HowNet. Lexical construction to indicate text’s topic. Markoff Random Walk to extend seed term set. Seed term extraction
Training corpus formed by BaiduZhidao daily log. Intent category based on SVM classification. intent category
Experiments results Testing corpus crawled from Sogoucompany. Table 1 Seed terms extraction • Table 2 Classification results
