Majigsuren Enkhsaikhan

TL;DR: In this paper, an iterative deep learning NER framework using distant supervision is proposed for automatic labelling of domain-specific datasets, which is applied to mineral exploration reports and produced a large BIO-annotated dataset with six geological categories.

TL;DR: In this article, natural language processing and deep learning methods are used to automatically extract and label geological terms with the correct entity types and establish the relationships between these entities, which is achieved by constructing knowledge graphs that describe geological entities and their relations as they appear in exploration reports.

TL;DR: For example, this paper used a pipeline-based approach to extract a set of triples from a given document and visualised useful information about each triple such as the degree, betweenness, structured relation type(s), and named entity types.

TL;DR: This paper investigated how representational learning of words can affect the entity query results from a large domain corpus of geological survey reports, and demonstrated the necessity of training domain-specific word embeddings, as pre-trained embeddeds are good at capturing morphological relations, but are inadequate for domain specific semantic relations.

TL;DR: An overview of the triple extraction system, which uses a pipeline-based approach to extract a set of triples from a given document and provides the facility to visualise useful information about each triple such as the degree, betweenness, structured relation type(s), and named entity types.