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Nlp Taxonomy Classifier

Developed by TimSchopf
This is a fine-tuned BERT-based language model for classifying NLP-related research papers according to concepts in the NLP taxonomy. It is a multi-label classifier capable of predicting concepts at all levels of the NLP taxonomy.
Downloads 173
Release Time : 7/11/2023

Model Overview

The model was fine-tuned on a weakly labeled dataset containing 178,521 scientific papers from ACL Anthology, arXiv cs.CL category, and Scopus. Before fine-tuning, the model was initialized with weights from allenai/specter2_base.

Model Features

Multi-label Classification
Capable of simultaneously predicting concepts at multiple levels in the NLP taxonomy, including hypernyms and hyponyms.
Weakly Supervised Learning
Trained on a large-scale weakly labeled dataset of scientific papers containing 178,521 papers.
Hierarchical Prediction
Automatically predicts hypernym concepts in the taxonomy when lower-level concepts are identified.

Model Capabilities

NLP Research Paper Classification
Multi-label Text Classification
Academic Literature Analysis

Use Cases

Academic Research
Research Field Analysis
Analyzing research trends and hot topics in the NLP field
Can automatically classify large volumes of papers to help researchers quickly understand field developments
Literature Recommendation System
Building personalized recommendation systems based on paper classification results
Improves researchers' efficiency in discovering relevant literature
Knowledge Management
Knowledge Graph Construction
Providing automatic annotation functionality for NLP knowledge graphs
Accelerates the construction and maintenance of knowledge graphs
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