id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ot7jvn6f25dqlag44iotnappoy	Omer Levy	Improving Distributional Similarity with Lessons Learned from Word Embeddings	2015	15	.pdf	application/pdf	9897	1241	68	outperform traditional count-based distributional models on word similarity and (2014) conducts a set of systematic experiments comparing word2vec embeddings to the more traditional distributional methods, such as pointwise To asses how each hyperparameter contributes to the algorithms' performance, we conduct a comprehensive set of experiments and compare four different representation methods, while Golberg (2014c) show that SGNS is implicitly factorizing a word-context matrix whose cell's values are shifted PMI. word and context vectors (e.g. SVD and SGNS). note that in the SVD-based factorization, the resulting word and context matrices have very different properties. Table 2: Performance of each method across different tasks in the "vanilla" scenario (all hyperparameters set to default): We begin by comparing the effect of various hyperparameter configurations, and observe that different settings have a substantial impact on performance (Section 5.1); at times, this improvement is greater than that of switching to a different representation method.	./cache/work_ot7jvn6f25dqlag44iotnappoy.pdf	./txt/work_ot7jvn6f25dqlag44iotnappoy.txt