In order to design strong paradigms for isolating lexical access and
semantics, we need to know what a word is. Surprisingly few linguists and
philosophers have a clear model of what a word is, even though words impact
basically every aspect of human life. Researchers that regularly publish
academic papers about language often rely on outdated, or inaccurate,
assumptions about wordhood. This short pedagogical document outlines what the
lexicon is most certainly not (though is often mistakenly taken to be), what it
might be (based on current good theories), and what some implications for
experimental design are.

为了设计有效的模型来隔离词汇访问和语义，我们需要知道什么是一个词。然而，令人惊讶的是，很少有语言学家和哲学家对词有一个清晰的模型，尽管词汇影响人类生活的方方面面。本文概述了词典绝对不是什么（尽管通常被错误地作为），它可能是什么（基于当前的良好理论），以及对实验设计的一些影响。

什么是一个词？

What is a word?

A word may contain one or more hidden concepts. While the "animal" word
evokes many images in our minds and encapsulates many concepts (birds, dogs,
cats, crocodiles, etc.), the `parrot' word evokes a single image (a colored
bird with a short, hooked beak and the ability to mimic sounds). In spoken or
written texts, we use some words in a general sense and some in a detailed way
to point to a specific object. Until now, a text's conceptual diversity value
cannot be determined using a standard and precise technique. This research
contributes to the natural language processing field of AI by offering a
standardized method and a generic metric for evaluating and comparing concept
diversity in different texts and domains. It also contributes to the field of
semantic research of languages. If we give examples for the diversity score of
two sentences, "He discovered an unknown entity." has a high conceptual
diversity score (16.6801), and "The endoplasmic reticulum forms a series of
flattened sacs within the cytoplasm of eukaryotic cells." sentence has a low
conceptual diversity score which is 3.9068.

这篇研究论文提供了一种标准化方法和一种通用度量工具，用于评估和比较不同文本和领域中的概念多样性，从而为人工智能自然语言处理领域做出了贡献，并为语义研究提供了参考。

一种用于文本概念多样性的新度量方法

A proposed new metric for the conceptual diversity of a text

The categorical compositional distributional model of meaning gives the
composition of words into phrases and sentences pride of place. However, it has
so far lacked a model of logical negation. This paper gives some steps towards
providing this operator, modelling it as a version of projection onto the
subspace orthogonal to a word. We give a small demonstration of the operators
performance in a sentence entailment task.

该研究论文提出了一种关于逻辑否定的操作方法，将其建模为对于词的正交子空间的投影版本，并在句子推理任务中展示了该操作符的性能。

面向组合分布语义的逻辑否定

Towards logical negation for compositional distributional semantics

In traditional Distributional Semantic Models (DSMs) the multiple senses of a
polysemous word are conflated into a single vector space representation. In
this work, we propose a DSM that learns multiple distributional representations
of a word based on different topics. First, a separate DSM is trained for each
topic and then each of the topic-based DSMs is aligned to a common vector
space. Our unsupervised mapping approach is motivated by the hypothesis that
words preserving their relative distances in different topic semantic
sub-spaces constitute robust \textit{semantic anchors} that define the mappings
between them. Aligned cross-topic representations achieve state-of-the-art
results for the task of contextual word similarity. Furthermore, evaluation on
NLP downstream tasks shows that multiple topic-based embeddings outperform
single-prototype models.

本文提出了一种基于不同主题学习单词的多个分布式表示的分布式语义模型，该模型通过不同主题训练不同的 DSM，然后将每个基于主题的 DSM 对齐到一个公共向量空间，实现了最先进的上下文词语相似性任务，并且在 NLP 下游任务中表现出比单一原型模型更好的性能。