In this paper, we describe DeFactoNLP, the system we designed for the FEVER
2018 Shared Task. The aim of this task was to conceive a system that can not
only automatically assess the veracity of a claim but also retrieve evidence
supporting this assessment from Wikipedia. In our approach, the Wikipedia
documents whose Term Frequency-Inverse Document Frequency (TFIDF) vectors are
most similar to the vector of the claim and those documents whose names are
similar to those of the named entities (NEs) mentioned in the claim are
identified as the documents which might contain evidence. The sentences in
these documents are then supplied to a textual entailment recognition module.
This module calculates the probability of each sentence supporting the claim,
contradicting the claim or not providing any relevant information to assess the
veracity of the claim. Various features computed using these probabilities are
finally used by a Random Forest classifier to determine the overall
truthfulness of the claim. The sentences which support this classification are
returned as evidence. Our approach achieved a 0.4277 evidence F1-score, a
0.5136 label accuracy and a 0.3833 FEVER score.

本文描述了 DeFactoNLP 系统，该系统用于自动评估声明的准确性并从维基百科中检索支持此评估的证据。我们的方法基于 TFIDF 向量和被提出的命名实体来识别包含证据的维基百科文章，并通过文本蕴涵识别模块来计算句子支持声明的概率。我们的方法在 FEVER 2018 共享任务中获得了不错的表现。