https://github.com/Aditi138/MorphologicalAnalysis
공식 코드CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Morphological Analysis is a central task in language processing that can take a word as input and detect the various morphological entities in the word and provide a morphological representation of it.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology
Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit Language
End-to-End Text Classification via Image-based Embedding using Character-level Networks
Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
LLMs' morphological analyses of complex FST-generated Finnish words
A Characterwise Windowed Approach to Hebrew Morphological Segmentation
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles
Neuro-evolutionary evidence for a universal fractal primate brain shape
Neuro-evolutionary evidence for a universal fractal primate brain shape
POS-tagging to highlight the skeletal structure of sentences
Learning to Recombine and Resample Data for Compositional Generalization
Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis
Cellpose+, a morphological analysis tool for feature extraction of stained cell images
The Role of Interpretable Patterns in Deep Learning for Morphology
Morphological Analyzer and Generator for Russian and Ukrainian Languages
LowResourceEval-2019: a shared task on morphological analysis for low-resource languages
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
Deep learning-based identification of sub-nuclear structures in FIB-SEM images
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
GEO-Nav: a geometric dataset of voltage-gated sodium channels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.