The phrase "out of domain" is correct and can be used in written English.
It typically refers to something that is not within a specified area or subject matter. Example: "The discussion about politics is out of domain in our workplace. Let's stick to work-related topics."
Based on the crystallizing concept exploration approach, this study implements an ontology construction mechanism that can automatically mine domain concepts out of domain document, determine relations between concept, and construct the domain ontology accordingly, thereby reducing cost and burden that would be incurred in a manual construction process.
"Primarily yes with regard to market intelligence while the innovation is based out of domain expertise to customize to a business requirement".
As expressed in one of the responses, "Primarily yes with regard to market intelligence while the innovation is based out of domain expertise to customize to a business requirement".
Instead, we use semi-supervised model adaptation; parameters are estimated using both unlabeled in-domain data (raw speech audio) and labeled out of domain data (text). In this work, we present a new semi-supervised language model adaptation procedure for Maximum Entropy models with n-gram features.
Although these efforts provide a means to of integrating chemical information and of breaking data out of domain or institutional data silos and exposing it for common searches, the triplified data often bears, to a greater or a lesser extent, the same problem as those found in the original databases.
"Running a newspaper is a pretty marginal business so Fairfax makes most of its cash out of Domain," says Professor of Urban Planning at Sydney University, Peter Phipps.
We augment the conventional maximum likelihood training criterion on out-of- domain text data with an additional term to minimize conditional entropy on in-domain audio.
For classification of these text segments to the in-domain and out-of domain area, document similarity with automatic thresholding are used.
Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models.
In the RREQ flooding attack, attackers would launch massive RREQ packets with out-of-domain IP address as its destination node.
Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments.
