An initial procedure in text-as-data applications is text preprocessing. One
of the typical steps, which can substantially facilitate computations, consists
in removing infrequent words believed to provide limited information about the
corpus. Despite popularity of vocabulary pruning, not many guidelines on how to
implement it are available in the literature. The aim of the paper is to fill
this gap by examining the effects of removing infrequent words for the quality
of topics estimated using Latent Dirichlet Allocation. The analysis is based on
Monte Carlo experiments taking into account different criteria for infrequent
terms removal and various evaluation metrics. The results indicate that pruning
is beneficial and that the share of vocabulary which might be eliminated can be
quite considerable.

研究论文通过模拟实验，考察了删除不常见词汇对使用潜在狄利克雷分配估计主题质量的影响，结果表明剪枝是有益的，并且可以消除相当大比例的词汇。

移除低频词对 LDA 模型主题质量的影响分析

Analysing the Impact of Removing Infrequent Words on Topic Quality in  LDA Models

In this paper, we propose methods for discovering semantic differences in
words appearing in two corpora based on the norms of contextualized word
vectors. The key idea is that the coverage of meanings is reflected in the norm
of its mean word vector. The proposed methods do not require the assumptions
concerning words and corpora for comparison that the previous methods do. All
they require are to compute the mean vector of contextualized word vectors and
its norm for each word type. Nevertheless, they are (i) robust for the skew in
corpus size; (ii) capable of detecting semantic differences in infrequent
words; and (iii) effective in pinpointing word instances that have a meaning
missing in one of the two corpora for comparison. We show these advantages for
native and non-native English corpora and also for historical corpora.

本文提出基于语境化词向量范数的语义差异发现方法，他们不需要先前方法中对单词和语料库进行比较的假设，对于语料库大小的偏斜鲁棒，能够检测不频繁出现的单词的语义差异，有效地找出在两个语料库之一缺失含义的单词实例。