https://github.com/mehdibenamorr/bert-positional-bias-ner
공식 코드Technical Report: Impact of Position Bias on Language Models in Token Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Part-of-speech tagging (POS tagging) is the task of tagging a word in a text with its part of speech. A part of speech is a category of words with similar grammatical properties. Common English parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.
Example:
| Vinken | , | 61 | years | old |
|---|---|---|---|---|
| NNP | , | CD | NNS | JJ |
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Technical Report: Impact of Position Bias on Language Models in Token Classification
Robust Multilingual Part-of-Speech Tagging via Adversarial Training
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning
Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing
Generalizing Natural Language Analysis through Span-relation Representations
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers
Minimax and Neyman-Pearson Meta-Learning for Outlier Languages
Learning to select data for transfer learning with Bayesian Optimization
A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging
Classification of US Supreme Court Cases using BERT-Based Techniques
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
A Tidy Data Model for Natural Language Processing using cleanNLP
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text
Linguistically-Informed Self-Attention for Semantic Role Labeling
OmniNet: A unified architecture for multi-modal multi-task learning
Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.