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Nested Propositions in Open Information Extraction
TLDR
NESTIE is proposed, which uses a nested representation to extract higher-order relations, and complex, interdependent assertions, and Nesting the extracted propositions allows NESTIE to more accurately reflect the meaning of the original sentence. Expand
Open Information Extraction from Question-Answer Pairs
TLDR
NeurON is described, a system for extracting tuples from question-answer pairs that combines distributed representations of a question and an answer to generate knowledge facts and is described on two real-world datasets that demonstrate that NeurON can find a significant number of new and interesting facts to extend a knowledge base compared to state-of-the-art OpenIE methods. Expand
Exploiting Structure in Representation of Named Entities using Active Learning
TLDR
This work proposes an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities and shows that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Expand
LUSTRE: An Interactive System for Entity Structured Representation and Variant Generation
TLDR
LUSTRE is an active learning based system that can learn the structured representations of entities interactively from a few labels and automatically generates programs to map entity mentions to their representations and to standardize them to a unique representation. Expand
SubjQA: A Dataset for Subjectivity and Review Comprehension
TLDR
This work investigates the relationship between subjectivity and QA, while developing a new dataset containing subjectivity annotations for questions and answer spans across 6 distinct domains, and releases an English QA dataset (SubjQA) based on customer reviews. Expand
Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases
TLDR
A novel KB-QA system, Multique, is presented, which can map a complex question to a complex query pattern using a sequence of simple queries each targeted at a specific KB. Expand
Learning to Answer Complex Questions over Knowledge Bases with Query Composition
TLDR
A KB-QA system, TextRay, is proposed, which answers complex questions using a novel decompose-execute-join approach and uses a semantic matching model which is able to learn simple queries using implicit supervision from question-answer pairs, thus eliminating the need for complex query patterns. Expand
Sampo: Unsupervised Knowledge Base Construction for Opinions and Implications
TLDR
This work proposes an unsupervised KBC system, SAMPO, that is tailored to build KBs for domains where many reviews on the same domain are available and shows that KBs generated using SAMPO can provide additional training data to fine-tune language models used for downstream tasks such as review comprehension. Expand
Online Schemaless Querying of Heterogeneous Open Knowledge Bases
TLDR
This paper introduces an online schemaless querying method that does not require the query to exactly match the facts and devise an alignment-based algorithm for extracting answers based on textual and semantic similarity of query components and evidence fields. Expand
Powering Effective Climate Communication with a Climate Knowledge Base
TLDR
This work aims to build a system that presents to any individual the climate information predicted to best motivate and inspire them to take action given their unique set of personal values, and relies on a knowledge base of causes and effects of climate change, and their associations to personal values. Expand