194 citations · 246 across the 11 of their papers we have counts for
11 papers
Learning Disentangled Representations of Negation and Uncertainty
Jake Vasilakes, Chrysoula Zerva, Makoto Miwa +1
Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically…
Physical Context and Timing Aware Sequence Generating GANs
Hayato Futase, Tomoki Tsujimura, Tetsuya Kajimoto +4
Generative Adversarial Networks (GANs) have shown remarkable successes in generating realistic images and interpolating changes between images. Existing models, however, do not tak…
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
Fusataka Kuniyoshi, Kohei Makino, Jun Ozawa +1
The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synth…
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection
Kurt Espinosa, Makoto Miwa, Sophia Ananiadou
We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search proble…
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text. Existing approaches use graph…
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa +1
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing…
A Walk-based Model on Entity Graphs for Relation Extraction
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All…
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Masaki Asada, Makoto Miwa, Yutaka Sasaki
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolut…
Bib2vec: An Embedding-based Search System for Bibliographic Information
Takuma Yoneda, Koki Mori, Makoto Miwa +1
We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this…
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Makoto Miwa, Mohit Bansal
We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree…
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
Kazuma Hashimoto, Pontus Stenetorp, Makoto Miwa +1
We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical re…