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End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
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 substructureExpand
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A Neural Layered Model for Nested Named Entity Recognition
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,Expand
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Event Extraction with Complex Event Classification Using Rich Features
Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, onlyExpand
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Protein-protein interaction extraction by leveraging multiple kernels and parsers
Protein-protein interaction (PPI) extraction is an important and widely researched task in the biomedical natural language processing (BioNLP) field. Kernel-based machine learning methods have beenExpand
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Event extraction across multiple levels of biological organization
Motivation: Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources andExpand
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A Rich Feature Vector for Protein-Protein Interaction Extraction from Multiple Corpora
Because of the importance of protein-protein interaction (PPI) extraction from text, many corpora have been proposed with slightly differing definitions of proteins and PPI. Since no single corpus isExpand
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Modeling Joint Entity and Relation Extraction with Table Representation
This paper proposes a history-based structured learning approach that jointly extracts entities and relations in a sentence. We introduce a novel simple and flexible table representation of entitiesExpand
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Reducing systematic review workload through certainty-based screening
Graphical abstract
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Word Embedding-based Antonym Detection using Thesauri and Distributional Information
This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonymsExpand
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Combining Multiple Layers of Syntactic Information for Protein-Protein Interaction Extraction
Protein-protein interaction extraction is a challenging information extraction task in the BioNLP field. Several kernels focusing on a part of syntactic information have been proposed for the task.Expand
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