Training on a combination of the existing data sets should help overcome this bias and produce more robust models than any trained on the individual corpora.

We present a systematic study of several Twitter POS data sets, the problems of label and data bias, discuss their effects on model performance, and show how to.

A systematic study of several Twitter POS data sets, the problems of label and data bias, and how to overcome them to learn models that perform well on ...

PDF | On Jan 1, 2014, Dirk Hovy and others published When POS datasets don't add up: Combatting sample bias | Find, read and cite all the research you need ...

When POS datasets don't add up: Combatting sample bias ... biases can we combine the existing data sets to also overcome sample bias. ... sample bias. We ...

We present a systematic study of several Twitter POS data sets, the problems of label and data bias, discuss their effects on model performance, and show how to ...

Beyond source selection bias, several aspects related to how data samples are collected from these sources have been questioned, including their ...

When POS data sets don't add up: Combatting sample bias. Author. Hovy, Dirk and Plank, Barbara and Søgaard, Anders. Conference. Proceedings of the Ninth ...

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[disjoint sets] In mathematics, two sets are said to be disjoint if they have no element in common. Equivalently, disjoint sets are sets whose intersection is ...