Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational
facts from large corpora. The technique well suits many open-world natural language …

Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …

We introduce a method for improving the structural understanding abilities of language
models. Unlike previous approaches that finetune the models with task-specific …

A recent state-of-the-art neural open information extraction (OpenIE) system generates
extractions iteratively, requiring repeated encoding of partial outputs. This comes at a …

Event representative learning aims to embed news events into continuous space vectors for
capturing syntactic and semantic information from text corpus, which is benefit to event …

Open relation extraction is the task of extracting open-domain relation facts from natural
language sentences. Existing works either utilize heuristics or distant-supervised …

While traditional systems for Open Information Extraction were statistical and rule-based,
recently neural models have been introduced for the task. Our work builds upon …

Abstract Progress with supervised Open Information Extraction (OpenIE) has been primarily
limited to English due to the scarcity of training data in other languages. In this paper, we …

Question answering over RDF data like knowledge graphs has been greatly advanced, with
a number of good systems providing crisp answers for natural language questions or …