https://github.com/ryanliut/wsd-ue
공식 코드Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation
Papers with Code 아카이브
Word Sense Disambiguation
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The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet.. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/s-mizuki-nlp/semantic_specialization_for_wsd
공식 코드Semantic Specialization for Knowledge-based Word Sense Disambiguation
https://github.com/sbos/AdaGram.jl
공식 코드https://github.com/schackow/sense-disambiguation-of-compound-constituents
공식 코드https://github.com/seantrott/raw-c
공식 코드RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)
https://github.com/semantic-web-company/wic-tsv
공식 코드WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context
https://github.com/seonghyeonye/flipped-learning
공식 코드Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
https://github.com/silasthu/admul
공식 코드Adversarial Multi-task Learning for End-to-end Metaphor Detection
https://github.com/somethingx02/topical_wordvec_models
공식 코드A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
https://github.com/soon91jae/uvwsd
공식 코드https://github.com/spandanagella/verse
공식 코드Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
https://github.com/stergioscha/oyxoy
공식 코드https://github.com/sunyilgdx/prompts4keras
공식 코드https://github.com/suytingwan/multilingual-wsd
공식 코드Multilingual Word Sense Disambiguation with Unified Sense Representation
https://github.com/tensorflow/lingvo
공식 코드https://github.com/tensorflow/mesh
공식 코드ST-MoE: Designing Stable and Transferable Sparse Expert Models
https://github.com/thomas-yin/semeval-2023-task1
공식 코드https://github.com/tosingithub/idesk
공식 코드Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
https://github.com/truas/MSSA
공식 코드Multi-sense embeddings through a word sense disambiguation process
https://github.com/tuanh23/perturbation-basedqe
공식 코드https://github.com/ualberta-nlp/v-wsd
공식 코드https://github.com/uhh-lt/bert-sense
공식 코드Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
https://github.com/uhh-lt/path2vec
공식 코드Making Fast Graph-based Algorithms with Graph Metric Embeddings
https://github.com/uhh-lt/wsd
공식 코드Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
https://github.com/vgupta123/contextualize_scdv
공식 코드https://github.com/yueyu1030/COSINE
공식 코드https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-1/ms-code-190
https://github.com/2023-MindSpore-1/ms-code-196
https://github.com/2023-MindSpore-1/ms-code-84
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2
https://github.com/ArvinZhuang/BiTAG
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/AshwinDeshpande96/Hierarchical-Softmax
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.