2 research outputs found
A Survey on Recent Named Entity Recognition and Relation Classification Methods with Focus on Few-Shot Learning Approaches
Named entity recognition and relation classification are key stages for
extracting information from unstructured text. Several natural language
processing applications utilize the two tasks, such as information retrieval,
knowledge graph construction and completion, question answering and other
domain-specific applications, such as biomedical data mining. We present a
survey of recent approaches in the two tasks with focus on few-shot learning
approaches. Our work compares the main approaches followed in the two
paradigms. Additionally, we report the latest metric scores in the two tasks
with a structured analysis that considers the results in the few-shot learning
scope
Multiple Relations Classification using Imbalanced Predictions Adaptation
The relation classification task assigns the proper semantic relation to a
pair of subject and object entities; the task plays a crucial role in various
text mining applications, such as knowledge graph construction and entities
interaction discovery in biomedical text. Current relation classification
models employ additional procedures to identify multiple relations in a single
sentence. Furthermore, they overlook the imbalanced predictions pattern. The
pattern arises from the presence of a few valid relations that need positive
labeling in a relatively large predefined relations set. We propose a multiple
relations classification model that tackles these issues through a customized
output architecture and by exploiting additional input features. Our findings
suggest that handling the imbalanced predictions leads to significant
improvements, even on a modest training design. The results demonstrate
superiority performance on benchmark datasets commonly used in relation
classification. To the best of our knowledge, this work is the first that
recognizes the imbalanced predictions within the relation classification task.Comment: