We present a new resource for the NLP community, namely a large (3.5M+
sentence) knowledge base of *generic statements*, e.g., "Trees remove carbon
dioxide from the atmosphere", collected from multiple corpora. This is the
first large resource to contain *naturally occurring* generic sentences, as
opposed to extracted or crowdsourced triples, and thus is rich in high-quality,
general, semantically complete statements. All GenericsKB sentences are
annotated with their topical term, surrounding context (sentences), and a
(learned) confidence. We also release GenericsKB-Best (1M+ sentences),
containing the best-quality generics in GenericsKB augmented with selected,
synthesized generics from WordNet and ConceptNet. In tests on two existing
datasets requiring multihop reasoning (OBQA and QASC), we find using GenericsKB
can result in higher scores and better explanations than using a much larger
corpus. This demonstrates that GenericsKB can be a useful resource for NLP
applications, as well as providing data for linguistic studies of generics and
their semantics. GenericsKB is available at
this https URL

我们提供了一个新的资源 —— 大型（3.5M + 句子）通用语句知识库，它包含从多个语料库中收集的自然生成的语句。这是第一个包含自然生成通用语句的大型资源，同时富含优质、通用、语义完整的语句。在两个需要多跳推理的现有数据集（OBQA 和 QASC）的测试中，使用 GenericsKB 可以得到比使用更大语料库更高的得分和更好的解释。这表明 GenericsKB 不仅可以成为 NLP 应用的有用资源，还可以为通用语句及其语义的语言研究提供数据。