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About 15 years a go, I had this job where I was requested to set up and administer an MQ connection from our company to the Depository Trust & Clearing Corporation (DTCC).Since I had no prior experience with MQ, I picked up the manual, learned a few commands, and in a day or so, had a script to create queue managers, queues, disk backing stores, etc.I got the system analysts (SA's) at both ends on the phone and in ten minutes had connectivity to their test and production environments.Access was applied for and granted to relevant individuals and applications, and application coding could begin.Two other machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at least when the input is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unique resource in the form of the Twi NL data set: a daily updated collection that probably contains at least 30% of the Dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013).
One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study (Goswami et al.
When using all user tweets, they reached an accuracy of 88.0%.
An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy of 92.0%.
172 For Tweets in Dutch, we first look at the official user interface for the Twi NL data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches.
These statistics are derived from the users profile information by way of some heuristics.
The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.