As human beings, we use our voices for conversation. When we interact with voice interfaces, therefore, our natural instinct is to apply the same rules that we would to a human conversation. We expect to be understood, but more than this, we expect the entity we're conversing with to remember the history of our conversation and understand the context of any following remarks. For some time, major search companies like Google and Bing have worked to teach their search engines to understand queries in natural language. Natural language search queries are queries that sound natural spoken aloud, such as, "How high is the Empire State building?"
We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Yahoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of queries and questions using the query-question click history. Elements of this corpus are automatically ranked according to recall and mean reciprocal rank, and then used for learning two independent learning to rank models (SVMRank), whereby a set of new query paraphrases can be scored according to recall and MRR. We perform several automatic evaluation procedures using cross-validation for analyzing the behavior of various aspects of our learned ranking functions, which show that our method is useful and effective for search in cQA.
We've gathered data from our newly created DSC search box, and based on 20,000 search queries over the last four months (most of them in the last 30 days), we discovered that the top queries so far are: The number in parenthesis indicates the number of queries, over the last four months. Note that some keywords have a high number of queries, because they are listed as top queries in one of our popular articles. Starred queries were not promoted in any way. Today we created a new data science search engine, ad-free, where anyone can submit his blog for indexation. We invite you to try it and share it.
Search engines understand very broad and generic information. This is why many of these search engine providers keep updating their search systems with different algorithms. For example Google search understands queries like movies, geography, images etc. This is not what a user wants. Users want to get more information and today's Google search engine cannot provide such information.