Search Results for author:
Found 15 papers, 8 papers with code
Event-Event Relation Extraction using Probabilistic Box Embedding
To understand a story with multiple events, it is important to capture the proper relations across these events.
Box-To-Box Transformations for Modeling Joint Hierarchies
In Patel et al., (2020), the authors demonstrate that only the transitive reduction is required and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Enhanced Distant Supervision with State-Change Information for Relation Extraction
In this work, we introduce a method for enhancing distant supervision with state-change information for relation extraction.
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes
We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models.
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
While vectors in Euclidean space can theoretically represent any graph, much recent work shows that alternatives such as complex, hyperbolic, order, or box embeddings have geometric properties better suited to modeling real-world graphs.
Box-To-Box Transformation for Modeling Joint Hierarchies
In Patel et al. (2020), the authors demonstrate that only the transitive reduction is required, and further extend box embeddings to capture joint hierarchies by augmenting the graph with new nodes.
Improving Local Identifiability in Probabilistic Box Embeddings
Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment.
Smoothing the Geometry of Probabilistic Box Embeddings
However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile.
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB).
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
In this paper, we instead use efficient truncated randomized search in this reward function to train structured prediction energy networks (SPENs), which provide efficient test-time inference using gradient-based search on a smooth, learned representation of the score landscape, and have previously yielded state-of-the-art results in structured prediction.
Word Embedding Perturbation for Sentence Classification
In this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods.
Bitext Name Tagging for Cross-lingual Entity Annotation Projection
Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.
Relation Classification via Recurrent Neural Network
Deep learning has gained much success in sentence-level relation classification.
Learning from LDA using Deep Neural Networks
Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference.
