In this paper I propose a new way of measuring linguistic productivity that
objectively assesses the ability of an affix to be used to coin new complex
words and, unlike other popular measures, is not directly dependent upon token
frequency. Specifically, I suggest that linguistic productivity may be viewed
as the probability of an affix to combine with a random base. The advantages of
this approach include the following. First, token frequency does not dominate
the productivity measure but naturally influences the sampling of bases.
Second, we are not just counting attested word types with an affix but rather
simulating the construction of these types and then checking whether they are
attested in the corpus. Third, a corpus-based approach and randomised design
assure that true neologisms and words coined long ago have equal chances to be
selected. The proposed algorithm is evaluated both on English and Russian data.
The obtained results provide some valuable insights into the relation of
linguistic productivity to the number of types and tokens. It looks like
burgeoning linguistic productivity manifests itself in an increasing number of
types. However, this process unfolds in two stages: first comes the increase in
high-frequency items, and only then follows the increase in low-frequency
items.

本文提出了一种新的衡量语言生产力的方式，可以客观评估词缀作为构词元素形成新的复杂词语的能力，并且不直接依赖于词汇频率。作者建议将语言生产力视为词缀与随机基础词组合的概率，这种方法的优点包括避免了词汇频率对生产力度量的主导作用，自然地影响基础词的采样，并且通过模拟构建这些类型并检查它们是否在语料库中得到确认，而不仅仅是计数词缀的已认证词汇类型。该算法在英语和俄语数据上进行了评估，研究结果为语言生产力与类型和词汇量之间的关系提供了一些有价值的见解，语言生产力的增长似乎体现在类型数量的增加上，但这个过程分为两个阶段：首先是高频项目的增加，然后才是低频项目的增加。

语言生产力的概率测量方法

Probabilistic Method of Measuring Linguistic Productivity

In the last decade, deep artificial neural networks have achieved astounding
performance in many natural language processing tasks. Given the high
productivity of language, these models must possess effective generalization
abilities. It is widely assumed that humans handle linguistic productivity by
means of algebraic compositional rules: Are deep networks similarly
compositional? After reviewing the main innovations characterizing current deep
language processing networks, I discuss a set of studies suggesting that deep
networks are capable of subtle grammar-dependent generalizations, but also that
they do not rely on systematic compositional rules. I argue that the intriguing
behaviour of these devices (still awaiting a full understanding) should be of
interest to linguists and cognitive scientists, as it offers a new perspective
on possible computational strategies to deal with linguistic productivity
beyond rule-based compositionality, and it might lead to new insights into the
less systematic generalization patterns that also appear in natural language.

该研究回顾了当前深度学习语言处理网络的主要创新，讨论了一系列研究表明深度网络能够进行微妙的语法依赖性泛化，但也不依赖于系统化的组合规则，这一现象应该引起语言学家和认知科学家的兴趣。