词向量

”可复现研究的数量远远低于科学界期望，我们创建了一个基于文本数据的机器学习模型，估计了自2000年以来心理学六个子领域中发布的超过14,000篇文章的可复现性分析。此外，我们还调查了可复现性与不同的研究方法、作者的生产力、引用影响力和机构声誉、论文的引用增长和社交媒体覆盖率有关的变化。我们的研究结果有助于建立大规模的经验模式，以便为推进复现研究提供依据。The number of manually replicated studies falls well below the abundance of important studies that the scientific community would like to see replicated. We created a text-based machine learning model to estimate the replication likelihood for more than 14,000 published articles in six subfields of Psychology since 2000. Additionally, we investigated how replicability varies with respect to different research methods, authors 'productivity, citation impact, and institutional prestige, and a paper’s citation growth and social media coverage. Our findings help establish large-scale empirical patterns on which to prioritize manual replications and advance replication research.“...

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Literally, **word embedding (Embeddings)** is the use of dense vectors to represent the semantics of a word. **Scholars have shown that by comparing the distance between these word vectors, we can understand how "humans" understand the meaning of words**. So, if we have a corpus comparing the distance between "taxes" and social groups ("conservatives", "socialists"), semantically, "taxes" should be farther away from "socialists", after all The money collected is for the service of the general public and has elements of socialism. In the word embedding space, word vectors contain rich information, such as analogies. Spain is to Madrid what Germany is to Berlin and France to Paris.字面上，**词嵌入(Embeddings)**是使用稠密向量表示一个词语的语义。**学者们已经表明，通过比较这些词向量之间的距离，我们可以了解“人类”如何理解单词的含义**。因此，如果我们有一个语料库，比较“税收” 与 社会团体(“保守派”、“社会主义者”) 之间的距离， 按照语义，“税收”应该距离 “社会主义者” 跟多一些，毕竟收上来的钱是为了社会大众服务，有社会主义的成分。在词嵌入空间中，词向量含有丰富的信息，例如可以做类比。西班牙之于马德里， 正如德国至于柏林、法国之于巴黎。"...

词嵌入是自然语言处理的一项基础技术。 其核心理念是根据大规模语料中词语和上下文的联系, 使用神经网络等机器学习算法自动提取有限维度的语义特征, 将每个词表示为一个低维稠密的数值向量(词向 量), 以用于后续分析。 心理学研究中, 词向量及其衍生的各种语义联系指标可用于探究人类的语义加工、认知判断、发散思维、社会偏见与刻板印象、社会与文化心理变迁等各类问题。 未来, 基于词嵌入技术的心理 学研究需要区分心理的内隐和外显成分, 深化拓展动态词向量和大型预训练语言模型(如 GPT、BERT)的应用, 并在时间和空间维度建立细粒度词向量数据库, 更多开展基于词嵌入的社会变迁和跨文化研究。 As a fundamental technique in natural language processing (NLP), word embedding quantifies a word as a low-dimensional, dense, and continuous numeric vector (i.e., word vector). Word embeddings can be obtained by using machine learning algorithms such as neural networks to predict the surrounding words given a word or vice versa (Word2Vec and FastText) or by predicting the probability of co-occurrence of multiple words (GloVe) in large-scale text corpora. Theoretically, the dimensions of a word vector reflect the pattern of how the word can be predicted in contexts; however, they also connote substantial semantic information of the word. Therefore, word embeddings can be used to analyze semantic meanings of text. In recent years, word embeddings have been increasingly applied to study human psychology, including human semantic processing, cognitive judgment, divergent thinking, social biases and stereotypes, and sociocultural changes at the societal or population level. Future research using word embeddings should (1) distinguish between implicit and explicit components of social cognition, (2) train fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) apply contextualized word embeddings and large pre-trained language models such as GPT and BERT. To enhance the application of word embeddings in psychology。

语言的文字反映了人类思想的结构，使我们能够在个人之间传递思想， 而使用大规模语料训练得来的词嵌入模型蕴含着这类信息。 英文的词嵌入在社会科学中的应用教程较多，大家可以谷歌查询，我主要想丰富中文数据的教程。The words of language reflect the structure of human thought, allowing us to transfer thoughts between individuals, and word embedding models trained using large-scale corpora contain this information. There are many application tutorials of English word embedding in social science. You can search it on Google. I mainly want to enrich the tutorials of Chinese data....