葡萄牙语中的半自动化事实核查：使用检索与声明提取进行语料库增强
Semi-automated Fact-checking in Portuguese: Corpora Enrichment using Retrieval with Claim extraction
AI总结：
本论文提出一种利用LLM提取声明并检索外部证据的方法，有效增强了葡萄牙语新闻事实核查语料库，并引入数据预处理框架提升质量。
摘要：
虚假信息的加速传播往往超出了人工事实核查的能力，凸显了对半自动化事实核查（SAFC）系统的迫切需求。在葡萄牙语语境中，整合外部证据的公开可用数据集明显匮乏，而外部证据是开发稳健AFC系统的关键组成部分，因为许多现有资源仅侧重于基于内在文本特征进行分类。本论文通过开发、应用和分析一种用外部证据增强葡萄牙语新闻语料库（Fake.Br、COVID19.BR、MuMiN-PT）的方法论来填补这一空白。该方法模拟用户的核查过程，采用大型语言模型（LLMs，具体为Gemini 1.5 Flash）从文本中提取主要声明，并利用搜索引擎API（Google Search API、Google FactCheck Claims Search API）检索相关的外部文档（证据）。此外，还引入了包含近似重复检测的数据验证与预处理框架，以提升基础语料库的质量。
英文摘要：
The accelerated dissemination of disinformation often outpaces the capacity for manual fact-checking, highlighting the urgent need for Semi-Automated Fact-Checking (SAFC) systems. Within the Portuguese language context, there is a noted scarcity of publicly available datasets that integrate external evidence, an essential component for developing robust AFC systems, as many existing resources focus solely on classification based on intrinsic text features. This dissertation addresses this gap by developing, applying, and analyzing a methodology to enrich Portuguese news corpora (Fake.Br, COVID19.BR, MuMiN-PT) with external evidence. The approach simulates a user's verification process, employing Large Language Models (LLMs, specifically Gemini 1.5 Flash) to extract the main claim from texts and search engine APIs (Google Search API, Google FactCheck Claims Search API) to retrieve relevant external documents (evidence). Additionally, a data validation and preprocessing framework, including near-duplicate detection, is introduced to enhance the quality of the base corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
