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New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long‐term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties—including researchers and the general public—focus their attention within the vast scientific literature. In this paper we present a system that...
This paper proposes a space-efficient, discriminatively enhanced topic model: a V structured topic model with an embedded log-linear component. The discriminative log-linear component reduces the number of parameters to be learnt while outperforming baseline generative models. At the same time, the explanatory power of the generative component is not compromised. We establish its superiority over...
Data quality is a perennial problem for many enterprise data assets. To improve data quality, businesses often employ rule based data standardization systems in which domain experts code rules for handling important and prevalent patterns. Finding these patterns is laborious and time consuming, particularly for noisy or highly specialized data sets. It is also subjective to the persons determining...
Enterprise datasets are often noisy. Several columns can have non-standard, erroneous or missing information. Poor quality data can lead to incorrect reporting and wrong conclusions being drawn. Data cleansing involves standardizing such data to improve its quality. Often data cleansing tasks involve writing rules manually. The step involves understanding the data quality issues and then writing data...
Record Linkage is an essential but expensive step in enterprise data management. In most deployments, blocking techniques are employed which can reduce the number of record pair comparisons and hence, the computational complexity of the task. Blocking algorithms require a careful selection of column(s) to be used for blocking. Selection of appropriate blocking column is critical to the accuracy and...
Enterprises today accumulate huge quantities of data which is often noisy and unstructured in nature making data cleansing an important task. Data cleansing refers to standardizing data from different sources to a common format so that data can be better utilized. Most of the enterprise data cleansing models are rule based involving lot of manual effort. Writing data quality rules is tedious task...
Data quality improvement is an important aspect of enterprise data management. Data characteristics can change with customers, with domain and geography making data quality improvement a challenging task. Data quality improvement is often an iterative process which mainly involves writing a set of data quality rules for standardization and elimination of duplicates that are present within the data...
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