3 research outputs found
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels
Interactive Extractive Search over Biomedical Corpora
We present a system that allows life-science researchers to search a
linguistically annotated corpus of scientific texts using patterns over
dependency graphs, as well as using patterns over token sequences and a
powerful variant of boolean keyword queries. In contrast to previous attempts
to dependency-based search, we introduce a light-weight query language that
does not require the user to know the details of the underlying linguistic
representations, and instead to query the corpus by providing an example
sentence coupled with simple markup. Search is performed at an interactive
speed due to efficient linguistic graph-indexing and retrieval engine. This
allows for rapid exploration, development and refinement of user queries. We
demonstrate the system using example workflows over two corpora: the PubMed
corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset, a
collection of over 45,000 research papers focused on COVID-19 research. The
system is publicly available at https://allenai.github.io/spik
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
Background: Automatic extraction of chemical-disease relations (CDR) from
unstructured text is of essential importance for disease treatment and drug
development. Meanwhile, biomedical experts have built many highly-structured
knowledge bases (KBs), which contain prior knowledge about chemicals and
diseases. Prior knowledge provides strong support for CDR extraction. How to
make full use of it is worth studying. Results: This paper proposes a novel
model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior
knowledge for CDR extraction. The proposed model first learns knowledge
representations including entity embeddings and relation embeddings from KBs.
Then, entity embeddings are used to control the propagation of context features
towards a chemical-disease pair with gated convolutions. After that, relation
embeddings are employed to further capture the weighted context features by a
shared attention pooling. Finally, the weighted context features containing
additional knowledge information are used for CDR extraction. Experiments on
the BioCreative V CDR dataset show that the proposed KCN achieves 71.28%
F1-score, which outperforms most of the state-of-the-art systems. Conclusions:
This paper proposes a novel CDR extraction model KCN to make full use of prior
knowledge. Experimental results demonstrate that KCN could effectively
integrate prior knowledge and contexts for the performance improvement.Comment: Published on BMC Bioinformatics, 16 pages, 5 figure