自动识别巴葡口语中三种否定结构，提升语言研究客观性
negativas: a prototype for searching and classifying sentential negation in speech data
- 基于NLP构建工具，区分 não 的三种语序位置
- 在22份访谈中识别3338个否定实例，准确率达93%
- 适合语言学研究者分析口语否定现象
否定是自然语言的普遍特征。在巴西葡萄牙语中，最常用的否定词是não，可作用于名词或动词。当作用于动词时，não有三种位置：句首（NEG1）、双重否定（NEG2）和句尾（NEG3），如não gosto、não gosto não、gosto não（我不喜欢）。从变异语言学视角看，这些结构是表达否定的不同形式；从语用角度看，它们分别承担礼貌和情态评价等交际功能。尽管语法上都可接受，但使用频率差异明显：NEG1在巴西各地占主导，而NEG2和NEG3较为罕见，暗示其使用受语境限制。低频特性导致研究常依赖主观判断，缺乏普适性。为此，我们开发了negativas工具，用于自动识别转录数据中的NEG1、NEG2和NEG3。工具开发包括四个阶段：（i）分析来自Falares Sergipanos数据库的22份访谈，由三位语言学家标注；（ii）利用自然语言处理技术编写代码；（iii）运行工具；（iv）评估准确率。标注者间一致性（Fleiss' Kappa）为0.57，属中等水平。工具共识别出3,338个não实例，其中2,085个被分类为NEG1、NEG2或NEG3，整体成功率达93%。然而，该工具存在局限：NEG1占已识别结构的91.5%，NEG2和NEG3分别占7.2%和1.2%；对NEG2识别困难，常误判为重叠结构（NEG1/NEG2/NEG3）。
原文摘要 · Abstract (English)
Negation is a universal feature of natural languages. In Brazilian Portuguese, the most commonly used negation particle is não, which can scope over nouns or verbs. When it scopes over a verb, não can occur in three positions: pre-verbal (NEG1), double negation (NEG2), or post-verbal (NEG3), e.g., não gosto, não gosto não, gosto não ("I do not like it"). From a variationist perspective, these structures are different forms of expressing negation. Pragmatically, they serve distinct communicative functions, such as politeness and modal evaluation. Despite their grammatical acceptability, these forms differ in frequency. NEG1 dominates across Brazilian regions, while NEG2 and NEG3 appear more rarely, suggesting its use is contextually restricted. This low-frequency challenges research, often resulting in subjective, non-generalizable interpretations of verbal negation with não. To address this, we developed negativas, a tool for automatically identifying NEG1, NEG2, and NEG3 in transcribed data. The tool's development involved four stages: i) analyzing a dataset of 22 interviews from the Falares Sergipanos database, annotated by three linguists, ii) creating a code using natural language processing (NLP) techniques, iii) running the tool, iv) evaluating accuracy. Inter-annotator consistency, measured using Fleiss' Kappa, was moderate (0.57). The tool identified 3,338 instances of não, classifying 2,085 as NEG1, NEG2, or NEG3, achieving a 93% success rate. However, negativas has limitations. NEG1 accounted for 91.5% of identified structures, while NEG2 and NEG3 represented 7.2% and 1.2%, respectively. The tool struggled with NEG2, sometimes misclassifying instances as overlapping structures (NEG1/NEG2/NEG3).
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