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Found 24 papers, 2 papers with code
Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora
This study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost.
Natural Adversarial Sentence Generation with Gradient-based Perturbation
This work proposes a novel algorithm to generate natural language adversarial input for text classification models, in order to investigate the robustness of these models.
On the Robustness of Self-Attentive Models
This work examines the robustness of self-attentive neural networks against adversarial input perturbations.
Revised JNLPBA Corpus: A Revised Version of Biomedical NER Corpus for Relation Extraction Task
Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application.
CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases
Sentiment lexicon is very helpful in dimensional sentiment applications.
MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network
Part-of-speech (POS) tagging and named entity recognition (NER) are crucial steps in natural language processing.
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model
In this work, we introduce a novel feature engineering approach named {``}algebraic invariance{''} to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning.
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory
In this paper, we propose a recurrent neural network model for identifying protein-protein interactions in biomedical literature.
Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
