Abstract Knowledge extraction is meant by acquiring relevant information from the
unstructured document in natural language and representing them in a structured form …

While a large number of knowledge graphs have previously been developed by
automatically extracting and structuring knowledge from literature, there is currently no such …

Distantly supervised named entity recognition (NER), which automatically learns NER
models without manually labeling data, has gained much attention recently. In distantly …

Massive amount of unstructured text data are generated daily, ranging from news articles to
scientific papers. How to mine structured knowledge from the text data remains a crucial …

As the AI revolution unfolds, the push toward automating support systems in diverse
professional fields ranging from open-source software to healthcare, and banking to …

We investigate the task of complex NER for the English language. The task is non-trivial due
to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities …

Distant supervision has been proven to be an efficient way of generating labeled instances
for Named Entity Recognition (NER). However, it suffers from dictionary biases and …

We introduce the task of detection of competing model entities from scientific documents. We
define competing models as those models that solve a particular task that is investigated in …

The relentless growth of scholarly publications, exemplified by the annual publication rate
exceeding 5 million articles, has posed a formidable challenge for researchers seeking …

Abstract Information Extraction (IE) techniques are essential to gleaning valuable
information about entities and their relationships from unstructured text and creating a …