Search Results for author:
Found 17 papers, 2 papers with code
Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models
To overcome this issue, our methods indirectly utilize distant supervision data with manually annotated training data.
Physical Context and Timing Aware Sequence Generating GANs
Furthermore, it is difficult to generate the changes at a specific timing and they often do not match with actual changes.
A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction
In this paper, we propose a novel edge-editing approach to extract relation information from a document.
Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature
Specifically, we focus on drug description and molecular structure information as the drug database information.
Ontology-Style Relation Annotation: A Case Study
In contrast, in our OSR annotation, a relation is annotated as a relation mention (i. e., not a link but a node) and domain and range links are annotated from the relation mention to its argument entity mentions.
SC-CoMIcs: A Superconductivity Corpus for Materials Informatics
This paper describes a novel corpus tailored for the text mining of superconducting materials in Materials Informatics (MI), named SuperConductivety Corpus for Materials Informatics (SC-CoMIcs).
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information.
Analyzing Well-Formedness of Syllables in Japanese Sign Language
We define a fine-grained feature set based on the hand-coded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained classifier.
Utilizing Visual Forms of Japanese Characters for Neural Review Classification
We propose a novel method that exploits visual information of ideograms and logograms in analyzing Japanese review documents.
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers
This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10.
Extracting Drug-Drug Interactions with Attention CNNs
We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model.
Bib2vec: An Embedding-based Search System for Bibliographic Information
We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.
Bib2vec: Embedding-based Search System for Bibliographic Information
We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.
Distributional Hypernym Generation by Jointly Learning Clusters and Projections
Most of the recent hypernym detection models focus on a hypernymy classification problem that determines whether a pair of words is in hypernymy or not.
Normalized Hierarchical SVM
We present improved methods of using structured SVMs in a large-scale hierarchical classification problem, that is when labels are leaves, or sets of leaves, in a tree or a DAG.
Modeling Joint Entity and Relation Extraction with Table Representation
Ranked #9 on
Relation Extraction
on CoNLL04
(NER Micro F1 metric)
