In many problems labeled data is scarce whereas unlabeled data is abundant.

Judge as of 2010. True: the machine learning literature of that time (for example, surveys of semi-supervised and unsupervised learning) widely recognises that in many practical problems labelled examples are costly and scarce, while unlabelled data is plentiful. Mostly true: this holds broadly, but 'many problems' slightly overstates how common it was. Mixed: the situation applied in some important domains but not in roughly as many others, so it is only half right as a general statement. Mostly false: it was true only in a few niche cases and is presented as general. False: the evidence shows that labelled data was generally not scarce compared with unlabelled data in the problems the field dealt with.

Set before the research began.

1. E1 Semi-supervised Learning Google Books (listing for Chapelle, Schölkopf, Zien)

   “Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics.”

2. E2 IR-Lab Project of Yanjun Qi (Fall 2004) Carnegie Mellon University (Yanjun Qi, IR-Lab project page) · 3 February 2004

   “Large amounts of unlabeled data are available in many real-life data-mining tasks.”

3. E3 IR-Lab Project of Yanjun Qi (Fall 2004) Carnegie Mellon University (Yanjun Qi, IR-Lab project page) · 3 February 2004

   “unlabeled data does not help classification when the unlabeled data distribution does not relate to the classification task”

4. E4 [PDF] Semisupervised learning - learning from labeled and unlabeled data Indian Statistical Institute, Bangalore (Saroj K. Meher slides)

   “Labeled data are expensive and limited that motivates to use”

E2 is a 2004 literature review of semi-supervised learning. It states that large amounts of unlabeled data are available in many real-life data-mining tasks. E1, the Google Books description of the semi-supervised learning book by Chapelle, Schölkopf and Zien, ties growing interest in the field to application domains where unlabeled data are plentiful, such as images, text and bioinformatics. E4 says labeled data are expensive and limited, though it is undated and so carries less weight. Taken together, these independent sources support the claim that in many problems labeled data is expensive and limited while unlabeled data is plentiful [E1][E2][E4], though only E2 is dated before 2010. E3's caveat is about whether unlabeled data helps, not how scarce labeled data is.