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Found 14 papers, 0 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.
Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network
Machine learning methods for financial document analysis have been focusing mainly on the textual part.
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews
With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
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.
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.
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.
