Makoto Miwa


2020

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Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
Fusataka Kuniyoshi | Kohei Makino | Jun Ozawa | Makoto Miwa
Proceedings of the 12th Language Resources and Evaluation Conference

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 synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger and simple heuristic rule-based relation extractor. Our experimental results demonstrate that the sequence tagger with the optimal setting can detect the entities with a macro-averaged F1 score of 0.826, while the rule-based relation extractor can achieve high performance with a macro-averaged F1 score of 0.887.

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Ontology-Style Relation Annotation: A Case Study
Savong Bou | Naoki Suzuki | Makoto Miwa | Yutaka Sasaki
Proceedings of the 12th Language Resources and Evaluation Conference

This paper proposes an Ontology-Style Relation (OSR) annotation approach. In conventional Relation Extraction (RE) datasets, relations are annotated as links between entity mentions. 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. We expect the following benefits: (1) the relation annotations can be easily converted to Resource Description Framework (RDF) triples to populate an Ontology, (2) some part of conventional RE tasks can be tackled as Named Entity Recognition (NER) tasks. The relation classes are limited to several RDF properties such as domain, range, and subClassOf, and (3) OSR annotations can be clear documentations of Ontology contents. As a case study, we converted an in-house corpus of Japanese traffic rules in conventional annotations into the OSR annotations and built a novel OSR-RoR (Rules of the Road) corpus. The inter-annotator agreements of the conversion were 85-87%. We evaluated the performance of neural NER and RE tools on the conventional and OSR annotations. The experimental results showed that the OSR annotations make the RE task easier while introducing slight complexity into the NER task.

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mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols
Mohammad Golam Sohrab | Anh-Khoa Duong Nguyen | Makoto Miwa | Hiroya Takamura
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all pos- sible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60% in terms of F-score as third rank system among the partic- ipated systems. In relation extraction task, we achieved 80.46% in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based in- formation extraction (DyGIE) systems.

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BENNERD: A Neural Named Entity Linking System for COVID-19
Mohammad Golam Sohrab | Khoa Duong | Makoto Miwa | Goran Topić | Ikeda Masami | Takamura Hiroya
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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. BEN- NERD mainly covers biomedical domain, es- pecially new entity types (e.g., coronavirus, vi- ral proteins, immune responses) by address- ing CORD-NER dataset. It includes several NLP tools to process biomedical texts includ- ing tokenization, flat and nested entity recog- nition, and candidate generation and rank- ing for EL that have been pre-trained using the CORD-NER corpus. To the best of our knowledge, this is the first attempt that ad- dresses NER and EL on COVID-19-related entities, such as COVID-19 virus, potential vaccines, and spreading mechanism, that may benefit research on COVID-19. We release an online system to enable real-time entity annotation with linking for end users. We also release the manually annotated test set and CORD-NERD dataset for leveraging EL task. The BENNERD system is available at https://aistairc.github.io/BENNERD/.

2019

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A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection
Kurt Junshean Espinosa | Makoto Miwa | Sophia Ananiadou
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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. Unlike existing structured prediction tasks such as dependency parsing, the task targets to detect DAG structures, which constitute events, from the relation graph. We define actions to construct events and use all the beams in a beam search to detect all event structures that may be overlapping and nested. The search process constructs events in a bottom-up manner while modelling the global properties for nested and overlapping structures simultaneously using neural networks. We show that the model achieves performance comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of any syntactic and hand-engineered features. Further analyses on the development set show that our model is more computationally efficient while yielding higher F1-score performance.

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Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Fenia Christopoulou | Makoto Miwa | Sophia Ananiadou
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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-based neural models with words as nodes and edges as relations between them, to encode relations across sentences. These models are node-based, i.e., they form pair representations based solely on the two target node representations. However, entity relations can be better expressed through unique edge representations formed as paths between nodes. We thus propose an edge-oriented graph neural model for document-level relation extraction. The model utilises different types of nodes and edges to create a document-level graph. An inference mechanism on the graph edges enables to learn intra- and inter-sentence relations using multi-instance learning internally. Experiments on two document-level biomedical datasets for chemical-disease and gene-disease associations show the usefulness of the proposed edge-oriented approach.

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A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models
Mohammad Golam Sohrab | Minh Thang Pham | Makoto Miwa | Hiroya Takamura
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

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. We proposed a neural NER model that captures the surrounding semantic information of a given sequence by capturing the forward- and backward-context of bidirectional LSTM (Bi-LSTM) output of a target span using contextual span representation-based exhaustive approach. The NER model enumerates all possible spans as potential entity mentions and classify them into entity types or no entity with deep neural networks. For representing span, we compare several different neural network architectures and their ensembling for the NER model. We then perform dictionary matching for CI and, if there is no matching, we further compute similarity scores between a mention and CUIs using entity embeddings to assign the CUI with the highest score to the mention. We evaluate our approach on the two sub-tasks in the shared task. Among the five submitted runs, the best run for each sub-task achieved the F-score of 86.76% on Sub-task 1 (NER) and the F-score of 79.97% (strict) on Sub-task 2 (CI).

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Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering
Hai-Long Trieu | Anh-Khoa Duong Nguyen | Nhung Nguyen | Makoto Miwa | Hiroya Takamura | Sophia Ananiadou
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

This paper describes our system developed for the coreference resolution task of the CRAFT Shared Tasks 2019. The CRAFT corpus is more challenging than other existing corpora because it contains full text articles. We have employed an existing span-based state-of-theart neural coreference resolution system as a baseline system. We enhance the system with two different techniques to capture longdistance coreferent pairs. Firstly, we filter noisy mentions based on parse trees with increasing the number of antecedent candidates. Secondly, instead of relying on the LSTMs, we integrate the highly expressive language model–BERT into our model. Experimental results show that our proposed systems significantly outperform the baseline. The best performing system obtained F-scores of 44%, 48%, 39%, 49%, 40%, and 57% on the test set with B3, BLANC, CEAFE, CEAFM, LEA, and MUC metrics, respectively. Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.

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Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Sunil Kumar Sahu | Fenia Christopoulou | Makoto Miwa | Sophia Ananiadou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.

2018

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A Neural Layered Model for Nested Named Entity Recognition
Meizhi Ju | Makoto Miwa | Sophia Ananiadou
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. 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. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively, in terms of F-score.

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Deep Exhaustive Model for Nested Named Entity Recognition
Mohammad Golam Sohrab | Makoto Miwa
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1% and 78.4% respectively in terms of F-score, without any external knowledge resources.

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A Walk-based Model on Entity Graphs for Relation Extraction
Fenia Christopoulou | Makoto Miwa | Sophia Ananiadou
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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 the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to l-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-of-the-art systems on the ACE 2005 dataset without using any external tools.

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Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Masaki Asada | Makoto Miwa | Yutaka Sasaki
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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 convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.

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Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts
Hai-Long Trieu | Nhung T. H. Nguyen | Makoto Miwa | Sophia Ananiadou
Proceedings of the BioNLP 2018 workshop

Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers. In this paper, we investigate the utility of the state-of-the-art general domain neural coreference resolution system on biomedical texts. The system is an end-to-end system without depending on any syntactic parsers. We also investigate the domain specific features to enhance the system for biomedical texts. Experimental results on the BioNLP Protein Coreference dataset and the CRAFT corpus show that, with no parser information, the adapted system compared favorably with the systems that depend on parser information on these datasets, achieving 51.23% on the BioNLP dataset and 36.33% on the CRAFT corpus in F1 score. In-domain embeddings and domain-specific features helped improve the performance on the BioNLP dataset, but they did not on the CRAFT corpus.

2017

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Bib2vec: Embedding-based Search System for Bibliographic Information
Takuma Yoneda | Koki Mori | Makoto Miwa | Yutaka Sasaki
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this model, we present a novel search system that shows the relationships among the elements in the ACL Anthology Reference Corpus. The evaluation results show that our model can achieve a high prediction ability and produce reasonable search results.

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Analyzing Well-Formedness of Syllables in Japanese Sign Language
Satoshi Yawata | Makoto Miwa | Yutaka Sasaki | Daisuke Hara
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

This paper tackles a problem of analyzing the well-formedness of syllables in Japanese Sign Language (JSL). We formulate the problem as a classification problem that classifies syllables into well-formed or ill-formed. We build a data set that contains hand-coded syllables and their well-formedness. 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. We also perform pseudo active learning to investigate the applicability of active learning in analyzing syllables. In the experiments, the best classifier with our combinatorial features achieved the accuracy of 87.0%. The pseudo active learning is also shown to be effective showing that it could reduce about 84% of training instances to achieve the accuracy of 82.0% when compared to the model without active learning.

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Utilizing Visual Forms of Japanese Characters for Neural Review Classification
Yota Toyama | Makoto Miwa | Yutaka Sasaki
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We propose a novel method that exploits visual information of ideograms and logograms in analyzing Japanese review documents. Our method first converts font images of Japanese characters into character embeddings using convolutional neural networks. It then constructs document embeddings from the character embeddings based on Hierarchical Attention Networks, which represent the documents based on attention mechanisms from a character level to a sentence level. The document embeddings are finally used to predict the labels of documents. Our method provides a way to exploit visual features of characters in languages with ideograms and logograms. In the experiments, our method achieved an accuracy comparable to a character embedding-based model while our method has much fewer parameters since it does not need to keep embeddings of thousands of characters.

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TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers
Tomoki Tsujimura | Makoto Miwa | Yutaka Sasaki
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. We participated in the full task setting of the entity segmentation, entity classification and relation classification (scenario 1) and the setting of relation classification only (scenario 3). The system was directly applied to the scenario 1 without modifying the codes thanks to its generality and flexibility. Our evaluation results show that the choice of appropriate pre-trained embeddings affected the performance significantly. With the best embeddings, our system was ranked third in the scenario 1 with the micro F1 score of 0.38. We also confirm that our system can produce the micro F1 score of 0.48 for the scenario 3 on the test data, and this score is close to the score of the 3rd ranked system in the task.

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Extracting Drug-Drug Interactions with Attention CNNs
Masaki Asada | Makoto Miwa | Yutaka Sasaki
BioNLP 2017

We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12%, which is competitive with the state-of-the-art models.

2016

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Ensemble Classification of Grants using LDA-based Features
Yannis Korkontzelos | Beverley Thomas | Makoto Miwa | Sophia Ananiadou
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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. Automating this classification process would save time and effort, providing the accuracy of the classifications is maintained. We employ five classification models to classify a set of BBSRC-funded research grants in 21 research topics based on unigrams, technical terms and Latent Dirichlet Allocation models. To boost precision, we investigate methods for combining their predictions into five aggregate classifiers. Evaluation confirmed that ensemble classification models lead to higher precision.It was observed that there is not a single best-performing aggregate method for all research topics. Instead, the best-performing method for a research topic depends on the number of positive training instances available for this topic. Subject matter experts considered the predictions of aggregate models to correct erroneous or incomplete manual assignments.

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Distributional Hypernym Generation by Jointly Learning Clusters and Projections
Josuke Yamane | Tomoya Takatani | Hitoshi Yamada | Makoto Miwa | Yutaka Sasaki
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We propose a novel word embedding-based hypernym generation model that jointly learns clusters of hyponym-hypernym relations, i.e., hypernymy, and projections from hyponym to hypernym embeddings. 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. These models do not directly deal with a hypernym generation problem in that a model generates hypernyms for a given word. Differently from previous studies, our model jointly learns the clusters and projections with adjusting the number of clusters so that the number of clusters can be determined depending on the learned projections and vice versa. Our model also boosts the performance by incorporating inner product-based similarity measures and negative examples, i.e., sampled non-hypernyms, into our objectives in learning. We evaluated our joint learning models on the task of Japanese and English hypernym generation and showed a significant improvement over an existing pipeline model. Our model also compared favorably to existing distributed hypernym detection models on the English hypernym classification task.

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End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Makoto Miwa | Mohit Bansal
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
Kazuma Hashimoto | Pontus Stenetorp | Makoto Miwa | Yoshimasa Tsuruoka
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Word Embedding-based Antonym Detection using Thesauri and Distributional Information
Masataka Ono | Makoto Miwa | Yutaka Sasaki
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Exploiting Timegraphs in Temporal Relation Classification
Natsuda Laokulrat | Makoto Miwa | Yoshimasa Tsuruoka
Proceedings of TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing

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Comparable Study of Event Extraction in Newswire and Biomedical Domains
Makoto Miwa | Paul Thompson | Ioannis Korkontzelos | Sophia Ananiadou
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures
Kazuma Hashimoto | Pontus Stenetorp | Makoto Miwa | Yoshimasa Tsuruoka
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Modeling Joint Entity and Relation Extraction with Table Representation
Makoto Miwa | Yutaka Sasaki
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Simple Customization of Recursive Neural Networks for Semantic Relation Classification
Kazuma Hashimoto | Makoto Miwa | Yoshimasa Tsuruoka | Takashi Chikayama
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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NaCTeM EventMine for BioNLP 2013 CG and PC tasks
Makoto Miwa | Sophia Ananiadou
Proceedings of the BioNLP Shared Task 2013 Workshop

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UTTime: Temporal Relation Classification using Deep Syntactic Features
Natsuda Laokulrat | Makoto Miwa | Yoshimasa Tsuruoka | Takashi Chikayama
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2011

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Towards Exhaustive Event Extraction for Protein Modifications
Sampo Pyysalo | Tomoko Ohta | Makoto Miwa | Jun’ichi Tsujii
Proceedings of BioNLP 2011 Workshop

2010

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Event Extraction for Post-Translational Modifications
Tomoko Ohta | Sampo Pyysalo | Makoto Miwa | Jin-Dong Kim | Jun’ichi Tsujii
Proceedings of the 2010 Workshop on Biomedical Natural Language Processing

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A Comparative Study of Syntactic Parsers for Event Extraction
Makoto Miwa | Sampo Pyysalo | Tadayoshi Hara | Jun’ichi Tsujii
Proceedings of the 2010 Workshop on Biomedical Natural Language Processing

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Evaluating Dependency Representations for Event Extraction
Makoto Miwa | Sampo Pyysalo | Tadayoshi Hara | Jun’ichi Tsujii
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Entity-Focused Sentence Simplification for Relation Extraction
Makoto Miwa | Rune Sætre | Yusuke Miyao | Jun’ichi Tsujii
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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A Rich Feature Vector for Protein-Protein Interaction Extraction from Multiple Corpora
Makoto Miwa | Rune Sætre | Yusuke Miyao | Jun’ichi Tsujii
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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From Protein-Protein Interaction to Molecular Event Extraction
Rune Sætre | Makoto Miwa | Kazuhiro Yoshida | Jun’ichi Tsujii
Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task