Detecting outlying subspaces for high-dimensional data: a heuristic search approach

[Abstract]: In this paper, we identify a new task for studying the out-lying degree of high-dimensional data, i.e. finding the sub-spaces (subset of features) in which given points are out-liers, and propose a novel detection algorithm, called High-D Outlying subspace Detection (HighDOD). We measure the outlying degree of the point using the sum of distances between this point and its k nearest neighbors. Heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been im- plemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top-down, bottom-up and random search methods. Points in these sparse subspaces are assumed to be the outliers. While knowing which data points are the outliers can be useful, in many applications, it is more important to identify the subspaces in which a given point is an outlier, which motivates the proposal of a new technique in this paper to handle this new task

Factors Influencing the Surprising Instability of Word Embeddings

Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspect of embedding spaces, namely their stability. We show that even relatively high frequency words (100-200 occurrences) are often unstable. We provide empirical evidence for how various factors contribute to the stability of word embeddings, and we analyze the effects of stability on downstream tasks.Comment: NAACL HLT 201

Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining