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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/A4Bio/OpenCPD
공식 코드Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement
https://github.com/AlanAnsell/PolyLM
공식 코드https://github.com/AlexMoreo/word-class-embeddings
공식 코드https://github.com/BPYap/BERT-WSD
공식 코드Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences
https://github.com/BingfengYan/DS_OCSORT
공식 코드Multiple Object Tracking Challenge Technical Report for Team MT_IoT
https://github.com/Dicta-Israel-Center-for-Text-Analysis/EACL_2023
공식 코드Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?
https://github.com/HSLCY/GlossBERT
공식 코드GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
https://github.com/LivNLP/Sense-based-Semantic-Change-Prediction
공식 코드Can Word Sense Distribution Detect Semantic Changes of Words?
https://github.com/Nithin-Holla/MetaWSD
공식 코드Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
https://github.com/RussianNLP/RussianSuperGLUE
공식 코드RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
https://github.com/Shreekavithaa/crowd-sourcing
공식 코드https://github.com/YDU-uva/VSM_WSD
공식 코드Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
https://github.com/YatinChaudhary/NCLM
공식 코드Explainable and Discourse Topic-aware Neural Language Understanding
https://github.com/akutuzov/russian_wsi
공식 코드Russian word sense induction by clustering averaged word embeddings
https://github.com/allenai/kb
공식 코드https://github.com/anastasiakrith/llm-for-vwsd
공식 코드Language Models as Knowledge Bases for Visual Word Sense Disambiguation
https://github.com/anastasiakrith/multimodal-retrieval-for-vwsd
공식 코드Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
https://github.com/aries-lm/lexmatcher-mt
공식 코드LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
https://github.com/arimuti/pejorativity
공식 코드PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets
https://github.com/asafamr/SymPatternWSI
공식 코드Word Sense Induction with Neural biLM and Symmetric Patterns
https://github.com/asafamr/bertwsi
공식 코드https://github.com/awslabs/sockeye
공식 코드Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures
https://github.com/begab/sparsity_makes_sense
공식 코드https://github.com/bramvanroy/fietje-2
공식 코드https://github.com/bsheludko/lexical-substitution
공식 코드https://github.com/clarin-pl/chatgpt-evaluation-01-2023
공식 코드https://github.com/clips/yarn
공식 코드Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts
https://github.com/componavt/wcorpus
공식 코드https://github.com/danielguzmanolivares/sandwich
공식 코드SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc
https://github.com/danlou/LMMS
공식 코드https://github.com/danlou/bert-disambiguation
공식 코드Analysis and Evaluation of Language Models for Word Sense Disambiguation
https://github.com/dbamman/latin-bert
공식 코드Latin BERT: A Contextual Language Model for Classical Philology
https://github.com/demelin/detecting_wsd_biases_for_nmt
공식 코드https://github.com/dingwc/multisense
공식 코드https://github.com/dustalov/watset
공식 코드Watset: Automatic Induction of Synsets from a Graph of Synonyms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.