7,460 research outputs found
Improving Negative Sampling for Word Representation using Self-embedded Features
Although the word-popularity based negative sampler has shown superb
performance in the skip-gram model, the theoretical motivation behind
oversampling popular (non-observed) words as negative samples is still not well
understood. In this paper, we start from an investigation of the gradient
vanishing issue in the skipgram model without a proper negative sampler. By
performing an insightful analysis from the stochastic gradient descent (SGD)
learning perspective, we demonstrate that, both theoretically and intuitively,
negative samples with larger inner product scores are more informative than
those with lower scores for the SGD learner in terms of both convergence rate
and accuracy. Understanding this, we propose an alternative sampling algorithm
that dynamically selects informative negative samples during each SGD update.
More importantly, the proposed sampler accounts for multi-dimensional
self-embedded features during the sampling process, which essentially makes it
more effective than the original popularity-based (one-dimensional) sampler.
Empirical experiments further verify our observations, and show that our
fine-grained samplers gain significant improvement over the existing ones
without increasing computational complexity.Comment: Accepted in WSDM 201
WordRank: Learning Word Embeddings via Robust Ranking
Embedding words in a vector space has gained a lot of attention in recent
years. While state-of-the-art methods provide efficient computation of word
similarities via a low-dimensional matrix embedding, their motivation is often
left unclear. In this paper, we argue that word embedding can be naturally
viewed as a ranking problem due to the ranking nature of the evaluation
metrics. Then, based on this insight, we propose a novel framework WordRank
that efficiently estimates word representations via robust ranking, in which
the attention mechanism and robustness to noise are readily achieved via the
DCG-like ranking losses. The performance of WordRank is measured in word
similarity and word analogy benchmarks, and the results are compared to the
state-of-the-art word embedding techniques. Our algorithm is very competitive
to the state-of-the- arts on large corpora, while outperforms them by a
significant margin when the training set is limited (i.e., sparse and noisy).
With 17 million tokens, WordRank performs almost as well as existing methods
using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node
distributed implementation of WordRank is publicly available for general usage.Comment: Conference on Empirical Methods in Natural Language Processing
(EMNLP), November 1-5, 2016, Austin, Texas, US
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin
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