Output-agreement mechanisms such as ESP Game have been widely used in human computation to obtain reliable human-generated labels.
This paper deals with the entity extraction task (named entity recognition) of a text mining process that aims at unveiling non-trivial semantic structures, such as relationships and interaction between entities or communities.
Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction.
This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation.
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering.