MinIE: Minimizing Facts in Open Information Extraction

Abstract

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from naturallanguage text in an unsupervised, domainindependent manner. In this paper, we propose MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.

Neural Open Information Extraction

“…The Open IE system was first introduced by TEXTRUNNER ( Banko et al, 2007), followed by several popular systems such as REVERB , OLLIE (Mausam et al, 2012), ClausIE (Del Corro and Gemulla, 2013) Stanford OPENIE (Angeli et al, 2015), PropS and most recently OPENIE4 1 (Mausam, 2016) and OPENIE5 2 . Although these systems have been widely used in a variety of applications, most of them were built on hand-crafted patterns from syntactic parsing, which causes errors in propagation and compounding at each stage (Banko et al, 2007;Gashteovski et al, 2017;Schneider et al, 2017). Therefore, it is essential to solve the problems of cascading errors to alleviate extracting incorrect tuples.…”

Integrating Local Context and Global Cohesiveness for Open Information Extraction

“…Angeli et al adopts a clause spli er using distant training and statistically maps predicate to known relation schemas [2]. MinIE [14] removes overly-speci c constituents and captures implicit relations in ClausIE by introducing several statistical measures like polarity, modality, a ribution, and quantities. Compared with these works, this paper di ers in several aspects: (1) previous work relies on external tools for phrase extraction, which may su er from domainshi and sparsity problem, while we provide an End-to-End solution towards Open IE.…”

EAL: A Toolkit and Dataset for Entity-Aspect Linking

“…EAL is implemented in Python. 2 It takes an entity in context as input and returns the most relevant aspect, relying on the following main functions:…”

“…As discussed in our previous work, we rely on the fact that in Wikipedia's Manual of Style and Linking, contributors are encouraged to point hyperlinks to the specific section/aspect addressed in text when present on the entity page. In order to leverage this data, we use OPIEC 5 [3] -the largest OIE corpus to date, containing more than 341M OIE triples -which was created by running MinIE [2] on the entire English Wikipedia. OPIEC retains the golden entity and entity-aspect links from within the Wikipedia articles (which have been created by Wikipedia contributors).…”