Dependency parsing is a central NLP task. In this paper we show that the common evaluation for unsupervised dependency parsing is highly sensitive to problematic annotations. We show that for three leading unsupervised parsers (Klein and Manning, 2004; Cohen and Smith, 2009; Spitkovsky et al., 2010a), a small set of parameters can be found whose modiﬁcation yields a signiﬁcant improvement in standard evaluation measures. These parameters correspond to local cases where no linguistic consensus exists as to the proper gold annotation. ...
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Reliability of capturing foot parameters using digital scanning and the neutral suspension casting technique...
Second, information should be thought of as better if it reduces the uncertainty
surrounding some future cost or benefit. For instance, future liabilities are inherently
uncertain. Information that can narrow the variance on estimates of those uncertain liabilities
should be considered better information. Reduced variance is particularly valuable when
decision-makers are risk-averse, since a reduction in variance alone can lead to different
decisions when there is risk aversion.
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