194 citations · 246 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
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…