Reasoning with latent structure refinement for document-level relation extraction
Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …
multiple sentences of a document and capturing complex interactions between inter …
Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction
Abstract Entities, as the essential elements in relation extraction tasks, exhibit certain
structure. In this work, we formulate such entity structure as distinctive dependencies …
structure. In this work, we formulate such entity structure as distinctive dependencies …
Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Document-level relation extraction is a complex human process that requires logical
inference to extract relationships between named entities in text. Existing approaches use …
inference to extract relationships between named entities in text. Existing approaches use …
Biomedical relation extraction via knowledge-enhanced reading comprehension
Background In biomedical research, chemical and disease relation extraction from
unstructured biomedical literature is an essential task. Effective context understanding and …
unstructured biomedical literature is an essential task. Effective context understanding and …
Document-level relation extraction with hierarchical dependency tree and bridge path
The inter-sentence relation in a document is characterized by complex contextual
information, large span of correlation and many kinds of relations, leading to the poor effect …
information, large span of correlation and many kinds of relations, leading to the poor effect …
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
DQ Nguyen, K Verspoor - arXiv preprint arXiv:1805.10586, 2018 - arxiv.org
We investigate the incorporation of character-based word representations into a standard
CNN-based relation extraction model. We experiment with two common neural …
CNN-based relation extraction model. We experiment with two common neural …
The dots have their values: Exploiting the node-edge connections in graph-based neural models for document-level relation extraction
The goal of Document-level Relation Extraction (DRE) is to recognize the relations between
entity mentions that can span beyond sentence boundary. The current state-of-the-art …
entity mentions that can span beyond sentence boundary. The current state-of-the-art …
Document-level relation extraction via graph transformer networks and temporal convolutional networks
Relation Extraction (RE) aims at extracting meaningful relation facts between entities in
texts. It is an important semantic processing task in the field of natural language processing …
texts. It is an important semantic processing task in the field of natural language processing …
Distantly supervised biomedical named entity recognition with dictionary expansion
State-of-the-art biomedical named entity recognition (BioNER) systems apply supervised
machine learning models (ie, relying on human effort for training data annotation) which are …
machine learning models (ie, relying on human effort for training data annotation) which are …
Open information extraction with meta-pattern discovery in biomedical literature
Biomedical open information extraction (BioOpenIE) is a novel paradigm to automatically
extract structured information from unstructured text with no or little supervision. It does not …
extract structured information from unstructured text with no or little supervision. It does not …