Papers with Code 아카이브
Part-Of-Speech Tagging
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 15
- 보존된 평가 기록
- 48
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 |
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling
TransCouplet:Transformer based Chinese Couplet Generation
To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP
Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data
Deep Learning Transformer Architecture for Named Entity Recognition on Low Resourced Languages: State of the art results
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
LaoPLM: Pre-trained Language Models for Lao
A Review on Part-of-Speech Technologies
SlovakBERT: Slovak Masked Language Model
GERNERMED -- An Open German Medical NER Model
reproducing "ner and pos when nothing is capitalized"
Cross-Register Projection for Headline Part of Speech Tagging
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
COMBO: State-of-the-Art Morphosyntactic Analysis
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
ELIT: Emory Language and Information Toolkit
How much pretraining data do language models need to learn syntax?
An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags
UzBERT: pretraining a BERT model for Uzbek
An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature
Differences in Chinese and Western tourists faced with Japanese hospitality: A natural language processing approach
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan
DaCy: A Unified Framework for Danish NLP
Specializing Multilingual Language Models: An Empirical Study
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
How to Adapt Your Pretrained Multilingual Model to 1600 Languages
Minimax and Neyman-Pearson Meta-Learning for Outlier Languages
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages
Weighted Training for Cross-Task Learning
On planetary systems as ordered sequences
The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.