Search Results for author: Makoto Miwa

Found 42 papers, 11 papers with code

Span-based Named Entity Recognition by Generating and Compressing Information

1 code implementation10 Feb 2023 Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou

For one type of IB model, we incorporate two unsupervised generative components, span reconstruction and synonym generation, into a span-based NER system.

named-entity-recognition Named Entity Recognition +1

Physical Context and Timing Aware Sequence Generating GANs

no code implementations28 Sep 2021 Hayato Futase, Tomoki Tsujimura, Tetsuya Kajimoto, Hajime Kawarazaki, Toshiyuki Suzuki, Makoto Miwa, Yutaka Sasaki

Furthermore, it is difficult to generate the changes at a specific timing and they often do not match with actual changes.

Analyzing Research Trends in Inorganic Materials Literature Using NLP

1 code implementation27 Jun 2021 Fusataka Kuniyoshi, Jun Ozawa, Makoto Miwa

In the field of inorganic materials science, there is a growing demand to extract knowledge such as physical properties and synthesis processes of materials by machine-reading a large number of papers.

named-entity-recognition Named Entity Recognition +3

A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction

1 code implementation Findings (ACL) 2021 Kohei Makino, Makoto Miwa, Yutaka Sasaki

In this paper, we propose a novel edge-editing approach to extract relation information from a document.


Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors

no code implementations NAACL 2021 Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou

We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs.

Multi-Task Learning Relation +2

BENNERD: A Neural Named Entity Linking System for COVID-19

no code implementations EMNLP 2020 Mohammad Golam Sohrab, Khoa Duong, Makoto Miwa, Goran Topi{\'c}, Ikeda Masami, Takamura Hiroya

We present a biomedical entity linking (EL) system BENNERD that detects named enti- ties in text and links them to the unified medical language system (UMLS) knowledge base (KB) entries to facilitate the corona virus disease 2019 (COVID-19) research.

Entity Linking NER

DeepEventMine: end-to-end neural nested event extraction from biomedical texts

1 code implementation17 Jun 2020 Hai-Long Trieu, Thy Thy Tran, Khoa N A Duong, Anh Nguyen, Makoto Miwa, Sophia Ananiadou

Motivation Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events.


Ontology-Style Relation Annotation: A Case Study

no code implementations LREC 2020 Savong Bou, Naoki Suzuki, Makoto Miwa, Yutaka Sasaki

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.

named-entity-recognition Named Entity Recognition +3

Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature

no code implementations LREC 2020 Fusataka Kuniyoshi, Kohei Makino, Jun Ozawa, Makoto Miwa

In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature.

Reading Comprehension Relation

A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models

no code implementations WS 2019 Mohammad Golam Sohrab, Minh Thang Pham, Makoto Miwa, Hiroya Takamura

We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities.

Entity Embeddings named-entity-recognition +3

A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection

no code implementations IJCNLP 2019 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 problem on a relation graph of trigger-argument structures.

Dependency Parsing Event Detection +3

Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network

no code implementations ACL 2019 Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou

Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.

Relation Relation Extraction +1

A Neural Layered Model for Nested Named Entity Recognition

1 code implementation NAACL 2018 Meizhi Ju, Makoto Miwa, Sophia Ananiadou

Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer.

Entity Linking named-entity-recognition +5

Analyzing Well-Formedness of Syllables in Japanese Sign Language

no code implementations IJCNLP 2017 Satoshi Yawata, Makoto Miwa, Yutaka Sasaki, Daisuke Hara

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.

Active Learning

Extracting Drug-Drug Interactions with Attention CNNs

no code implementations WS 2017 Masaki Asada, Makoto Miwa, Yutaka Sasaki

We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model.

Feature Engineering General Classification +2

Bib2vec: An Embedding-based Search System for Bibliographic Information

no code implementations16 Jun 2017 Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki

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

no code implementations EACL 2017 Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki

We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.

Network Embedding Topic Models

Distributional Hypernym Generation by Jointly Learning Clusters and Projections

no code implementations COLING 2016 Josuke Yamane, Tomoya Takatani, Hitoshi Yamada, Makoto Miwa, Yutaka Sasaki

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.

Clustering General Classification +2

Ensemble Classification of Grants using LDA-based Features

no code implementations LREC 2016 Yannis Korkontzelos, Beverley Thomas, Makoto Miwa, Sophia Ananiadou

Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making.

Classification Decision Making +1

End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

2 code implementations ACL 2016 Makoto Miwa, Mohit Bansal

We present a novel end-to-end neural model to extract entities and relations between them.

 Ranked #1 on Relation Extraction on ACE 2005 (Sentence Encoder metric)

Relation Relation Classification

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