NEW YORK – Facebook has paid contractors to transcribe audio clips from users of its Messenger service, raising privacy concerns for a company with a history of privacy lapses. The practice was, until recently, common in the tech industry. Companies say the practice helps improve their services. But users aren't typically aware that humans and not just computers are reviewing audio. Transcriptions done by humans raise bigger concerns because of the potential of rogue employees or contractors leaking details.
We see in Code Snippet 1 how we want to call into the adder method in the JavaTest1 class, passing in two parameters: @x, and @y. When we execute the code the Java C language extension gets the parameter values, and looks in the code for two class-level variables named x, and y, and assigns the values to those variables. The adder method then uses x, and y. I mentioned above how a couple of things changed after Microsoft introduced the Java Language SDK. One of them was that you no longer define a method in SPEES's @script parameter. The parameter instead defines a class you want to call into.
Search engines present fix-length passages from documents ranked by relevance against the query. In this paper, we present and compare novel, language-model based methods for extracting variable length document snippets by real-time processing of documents using the query issued by the user. With this extra level of information, the returned snippets are considerably more informative. Unlike previous work on passage retrieval which relies on searching relevant segments for filtering of preoccupied passages, we focus on query-informed segmentation to extract context-aware relevant snippets with variable length. In particular, we show that, when informed through an appropriate relevance language model, curvature analysis and Hidden Markov model (HMM) based content segmentation techniques can facilitate to extract relevant document snippets.
Document clustering has been applied in web information retrieval, which facilitates users' quick browsing by organizing retrieved results into different groups. Meanwhile, a tree-like hierarchical structure is wellsuited for organizing the retrieved results in favor of web users. In this regard, we introduce a new method for hierarchical clustering of web snippets by exploiting a phrase-based document index. In our method, a hierarchy of web snippets is built based on phrases instead of all snippets, and the snippets are then assigned to the corresponding clusters consisting of phrases. We show that, as opposed to the traditional hierarchical clustering, our method not only presents meaningful cluster labels but also improves clustering performance.