Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-ofthe-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we introduce two simple syntactic and lexical constraints on binary relations expressed by verbs. We implemented the constraints in the REVERB Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TEXTRUNNER and WOE pos . More than 30% of REVERB's extractions are at precision 0.8 or highercompared to virtually none for earlier systems. The paper concludes with a detailed analysis of REVERB's errors, suggesting directions for future work.
ReVerb REF is the main representative of this approach that uses syntactic and lexical constraints to extract arguments and relations expressed by verbs in English sentences.
Identifying Relations for Open Information Extraction
EMNLP
Computer Science