ReNoun: Fact Extraction for Nominal Attributes
By Mohamed Yahya, Steven Euijong Whang, Rahul Gupta, Alon Y. Halevy. 2014
104 citations recorded by the source corpus.
Search engines are increasingly relying on large knowledge bases of facts to provide direct answers to users’ queries. However, the construction of these knowledge bases is largely manual and does not scale to the long and heavy tail of facts. Open information extraction tries to address this challenge, but typically assumes that facts are expressed with verb phrases, and therefore has had difficulty extracting facts for noun-based relations. We describe ReNoun, an open information extraction system that complements previous efforts by focusing on nominal attributes and on the long tail. ReNoun’s approach is based on leveraging a large ontology of noun attributes mined from a text corpus and from user queries. ReNoun creates a seed set of training data by using specialized patterns and requiring that the facts mention an attribute in the ontology. ReNoun then generalizes from this seed set to produce a much larger set of extractions that are then scored. We describe experiments that show that we extract facts with high precision and for attributes that cannot be extracted with verb-based techniques.
Subfields: Artificial Intelligence
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Citations in the corpus
- A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Ollie [134], ReVerb [135], and ReNoun [136] and open information extraction systems that apply patterns to Web text.”
- Scalable Semantic Parsing with Partial Ontologies (2015, background): “A notable exception to this trend is the ReNoun sys-tem (Yahya et al., 2014) which models noun phrase structure for open information extraction.”
- Supervised Open Information Extraction (2018, background): “…case of verbal binary propositions (i.e, subject-verb-object tuples), some systems specialize in other syntactic constructions, including noun-mediated relations (Yahya et al., 2014; Pal and Mausam, 2016), n-ary relations (Akbik and L¨oser, 2012), or nested propositions (Bhutani et al., 2016).”
- Fonduer: Knowledge Base Construction from Richly Formatted Data (2017, unknown): “Context Scope Existing KBC systems often restrict candidates to specific context scopes such as single sentences [23, 44] or tables [7].”