Latent semantics of action verbs reflect phonetic parameters of intensity and emotional content

Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 x 20 emotion, face, and hand related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora; HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes

当我们唤起思绪时，语言实则映射着词汇共现的统计规律，而这些规律又反过来塑造了我们对世界的认知图景。无论是统计谷歌搜索查询中名词与动词的组合频次，还是从由维基百科文本与莎士比亚戏剧文本构成的词项-文档矩阵中提取特征向量，所得到的潜在语义（latent semantics）不仅能够捕捉构建概念的关联联系，还能揭示潜藏于语言表层结构之中的空间维度。已有研究发现，幼儿阶段便能感知物体形态与运动和语音对立之间的关联，本研究则旨在探究：发音器官运动参数与声学参数是否同样能够区分动作动词的潜在语义。本研究选取3组各20个分别与情感、面部、手部相关的动词（这类动词已知可激活大脑运动前区），采用潜在语义分析（Latent Semantic Analysis, LSA）计算这些动词间的两两余弦相似度，并基于HAWIK与TASA两个大规模文本语料库，对生成的邻接矩阵（adjacency matrices）进行对比分析。通过层次聚类（hierarchical clustering）识别两个语料库中的共同结构，结果显示这些动词大致可分为两类：一类涉及口部与手部协同运动，另一类则对应情绪表达。将动词拆解为其构成音素后，聚类得到的小幅度与大幅度运动类动词，可通过前元音与后元音的差异加以区分，而这两类元音恰好对应唤醒水平的高低变化。而聚类得到的情绪类动词，则呈现出以闭口与开口颌位生成的音素序列为特征的规律，这类音素会引发共振峰频率（formant frequencies）的上升或下降，进而可能影响其感知到的情绪效价（valence）。该研究结果表明，动作动词的潜在语义实则反映了强度与情绪极性两类参数，而这些参数与音素的发音对立特征及声学特性存在显著关联。