We show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. RE-Flex uses contextual matching to ensure that language model predictions matches supporting evidence from the input corpus that is relevant to a target relation. We perform an extensive experimental study over multiple relation extraction benchmarks and demonstrate that RE-Flex outperforms competing unsupervised relation extraction methods based on pretrained language models by up to 27.8 F 1 points compared to the next-best method. Our results show that constrained inference queries against a language model can enable accurate unsupervised relation extraction.
A sentiment analysis is a study which includes opinion mining, sentiment classification, and opinion summarization broadly. An opinion summarization plays an increasing research interest for automatically compressing the extensive information and generating a short summary with unlimited time. Opinion analysis is one of the emerging studies in computer domain which embrace of sentiment polarity, sentiment, opinion or semantic orientation. This paper presents the survey on sentiment analysis and summarization approaches with its challenges, methodology and pros and cons of the existing methodology. In this survey, we evaluated the research gaps of the existing technique for suggesting the new technique by the mean of applying the semi-supervised data undergo clustering; classification and summarization by means of convolutional neural network (CNN) network learning method which may use for the opinion summarization.
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