A large number of Open Relation Extraction approaches have been proposed recently, covering a wide range of NLP machinery, from “shallow” (e.g., part-of-speech tagging) to “deep” (e.g., semantic role labeling–SRL). A natural question then is what is the tradeoff between NLP depth (and associated computational cost) versus effectiveness. This paper presents a fair and objective experimental comp...

We derive a thermodynamic uncertainty relation for general open quantum dynamics, described by joint unitary evolution on composite system comprising and an environment. By measuring the environmental state after system-environment interaction, we bound counting observables in environment survival activity, which reduces to dynamical activity classical Markov processes. Remarkably, derived here...

Open Relation Extraction (ORE) task remains a challenge to obtain semantic representation by discovering arbitrary relations from the unstructured text. Conventional methods heavily depend on feature engineering or syntactic parsing, which are inefficient error-cascading. Recently, leveraging supervised deep learning address ORE is promising way. However, there two main challenges: (1) The lack...

Mining the semantics of open relations is an important task in open information extraction (Open IE). For this task, the difficulty is that the expressions of a specific semantic in free texts are always not unique. Therefore, it needs us to deeply capture the semantics behind the various expressions. In this paper, we propose an open relation mapping method combining the instances and semantic...

This paper presents a novel syntactic logical relation for System F◦, a simple variant of the linear polymorphic λcalculus. We define a logical relation for open values under both open linear and unrestricted contexts, then extend it for open terms with evaluation and open relation substitutions. Relations that instantiate type quantifiers are for open terms and types. We demonstrate the applic...

Web texts typically undergo the open-ended growth of new relations. Traditional relation extraction methods lack automatic annotation and perform poorly on tasks. We propose an open-domain system (ORES) based distant supervision few-shot learning to solve this problem. More specifically, we utilize tBERT design instance selector 1, implementing labeling in data mining component. Meanwhile, exam...