Convolutional Bi-directional LSTM for Detecting Inappropriate Query Suggestions in Web Search

Harish Yenala, Manoj Kumar Chinnakotla, Jay Goyal

Abstract: A web search query is considered inappropriate if it may cause anger, annoyance to certain users or exhibits lack of respect, rudeness, discourteousness towards certain individuals/communities or may be capable of inflicting harm to oneself or others. A search engine should regulate its query completion suggestions by detecting and filtering such queries as it may hurt the user sentiments or may lead to legal issues thereby tarnishing the brand image. Hence, automatic detection and pruning of such inappropriate queries from completions and related search suggestions is an important problem for most commercial search engines. The problem is rendered difficult due to unique challenges posed by search queries such as lack of sufficient context, natural language ambiguity and presence of spelling mistakes and variations.

External IDs:dblp:conf/pakdd/YenalaCG17