Lisa Seung-Yeon Lee | Searcharxiv

arXiv2015

Recognizing signed-graphic matroids: Cylinder flips and the importance of column scaling

In this paper, we investigate the importance of column scaling in relating two signed-graphic representations of the same matroid. We used the Sage Mathematics software to generate many examples of signed-graphic matroids and their signed-graphic representations. Our examples show that column scaling is sometimes necessary in order to transform one signed-graphic representation into another; moreover, there exist many collections of signed-graphic representations that row-reduce to the same standard form. We also discuss an interesting matroid-preserving operation on a signed graph, which we call the cylinder flip, that relates certain pairs of signed-graphic representations of the same matroid.

math.CO↗

arXiv2015

On the Linear Algebraic Structure of Distributed Word Representations

In this work, we leverage the linear algebraic structure of distributed word representations to automatically extend knowledge bases and allow a machine to learn new facts about the world. Our goal is to extract structured facts from corpora in a simpler manner, without applying classifiers or patterns, and using only the co-occurrence statistics of words. We demonstrate that the linear algebraic structure of word embeddings can be used to reduce data requirements for methods of learning facts. In particular, we demonstrate that words belonging to a common category, or pairs of words satisfying a certain relation, form a low-rank subspace in the projected space. We compute a basis for this low-rank subspace using singular value decomposition (SVD), then use this basis to discover new facts and to fit vectors for less frequent words which we do not yet have vectors for.

cs.CL↗