SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
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Part-Of-Speech TaggingWord Sense DisambiguationSimilar Papers 제목 키워드 기반
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models
2016-06-01 · SEMEVAL 2016 6
· Xin Tang, Fei Li, Donghong Ji
Feature EngineeringMachine TranslationSemantic Parsing
ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings
2016-06-01 · SEMEVAL 2016 6
· Angelika Kirilin, Felix Krauss, Yannick Versley
Named Entity Recognition (NER)Word EmbeddingsWord Sense Disambiguation
BERT(s) to Detect Multiword Expressions
2022-08-16
· Damith Premasiri, Tharindu Ranasinghe
Multiword expressions (MWEs) present groups of words in which the meaning of the whole is not derived from the meaning of its parts. The task of processing MWEs is crucial in many natural language processing (NLP) applic…
Machine TranslationTranslationFrom Incremental Meaning to Semantic Unit (phrase by phrase)
2016-04-17
· Andreas Scherbakov, Ekaterina Vylomova, Fei Liu, Timothy Baldwin
This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10. The approach is primarily based on a combination of …
Word EmbeddingsBYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles
2020-12-01 · SEMEVAL 2020
· Yang Bai, Xiaobing Zhou
We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals). This paper examines different approaches and models towards…
Classificationcounterfactual