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

During the recent era of big data, a huge volume of unstructured data are being produced in
various forms of audio, video, images, text, and animation. Effective use of these …

Information extraction (IE) is a challenging task, particularly when dealing with highly
heterogeneous data. State-of-the-art data mining technologies struggle to process …

In this study, text mining and visualization technology is applied to extract valuable
information otherwise buried in the dense and abstract form of construction report text and …

In natural language processing, text summarization is an important application used to
extract desired information by reducing large text. Existing studies use keyword-based …

Extracting valuable insights from vast amounts of unstructured digital text presents
significant challenges across diverse domains. This research addresses this challenge by …

Abstract Open Information Extraction (OpenIE) represents a crucial NLP task aimed at
deriving structured information from unstructured text, unrestricted by relation type or …

The number of documents published on the Web in languages other than English grows
every year. As a consequence, the need to extract useful information from different …

Open information extraction is an important NLP task that targets extracting structured
information from unstructured text without limitations on the relation type or the domain of the …

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.
Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying …