Despite the success of distributional semantics, composing phrases from word
vectors remains an important challenge. Several methods have been tried for
benchmark tasks such as sentiment classification, including word vector
averaging, matrix-vector approaches based on parsing, and on-the-fly learning
of paragraph vectors. Most models usually omit stop words from the composition.
Instead of such an yes-no decision, we consider several graded schemes where
words are weighted according to their discriminatory relevance with respect to
its use in the document (e.g., idf). Some of these methods (particularly
tf-idf) are seen to result in a significant improvement in performance over
prior state of the art. Further, combining such approaches into an ensemble
based on alternate classifiers such as the RNN model, results in an 1.6%
performance improvement on the standard IMDB movie review dataset, and a 7.01%
improvement on Amazon product reviews. Since these are language free models and
can be obtained in an unsupervised manner, they are of interest also for
under-resourced languages such as Hindi as well and many more languages. We
demonstrate the language free aspects by showing a gain of 12% for two review
datasets over earlier results, and also release a new larger dataset for future
testing (Singh,2015).

研究使用基于加权单词向量的模型，并使用多种分类器进行集成，以在影评数据集上提高 1.6％的性能，并在 Amazon 产品评论中提高 7.01％的性能，其中 tf-idf 等方法可以显着提高先前技术水平。

单词不平等：适用于构建组合文档向量的分级加权模型

Words are not Equal: Graded Weighting Model for building Composite  Document Vectors

Current distributed representations of words show little resemblance to
theories of lexical semantics. The former are dense and uninterpretable, the
latter largely based on familiar, discrete classes (e.g., supersenses) and
relations (e.g., synonymy and hypernymy). We propose methods that transform
word vectors into sparse (and optionally binary) vectors. The resulting
representations are more similar to the interpretable features typically used
in NLP, though they are discovered automatically from raw corpora. Because the
vectors are highly sparse, they are computationally easy to work with. Most
importantly, we find that they outperform the original vectors on benchmark
tasks.

提出一种将词向量转换成稀疏（可选二进制）向量的方法，使得词向量更接近于自然语言处理中常用的可解释特征，但这些特征是从原始语料库中自动发现的，并且在基准任务上优于原始向量。

稀疏过完备词向量表示

Sparse Overcomplete Word Vector Representations

Distributed representations of words have boosted the performance of many
Natural Language Processing tasks. However, usually only one representation per
word is obtained, not acknowledging the fact that some words have multiple
meanings. This has a negative effect on the individual word representations and
the language model as a whole. In this paper we present a simple model that
enables recent techniques for building word vectors to represent distinct
senses of polysemic words. In our assessment of this model we show that it is
able to effectively discriminate between words' senses and to do so in a
computationally efficient manner.

本文介绍了一种简单的模型，它可以代表多义词的不同含义，有效地区分单词的语义，并保证计算效率。