arXiv:2405.18308
Joint Lemmatization and Morphological Tagging with LEMMING
원문 초록
We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. LEMMING sets the new state of the art in token-based statistical lemmatization on six languages; e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Heidelberg-Boston @ SIGTYP 2024 Shared Task: Enhancing Low-Resource Language Analysis With Character-Aware Hierarchical Transformers
Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models
Context Aware Lemmatization and Morphological Tagging Method in Turkish
LatinCy: Synthetic Trained Pipelines for Latin NLP
토론
이 논문과 연결된 포럼 글이 아직 없습니다.