Search Results for author:
Found 12 papers, 1 papers with code
Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations
In this paper, we demonstrate that by utilizing sparse word representations, it becomes possible to surpass the results of more complex task-specific models on the task of fine-grained all-words word sense disambiguation.
Ranked #8 on
Word Sense Disambiguation
on Supervised:
SzegedAI at SemEval-2021 Task 2: Zero-shot Approach for Multilingual and Cross-lingual Word-in-Context Disambiguation
In this paper, we introduce our system that we participated with at the multilingual and cross-lingual word-in-context disambiguation SemEval 2021 shared task.
Changing the Basis of Contextual Representations with Explicit Semantics
The application of transformer-based contextual representations has became a de facto solution for solving complex NLP tasks.
300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes
This paper describes 300-sparsians{'}s participation in SemEval-2018 Task 9: Hypernym Discovery, with a system based on sparse coding and a formal concept hierarchy obtained from word embeddings.
Ranked #3 on
Hypernym Discovery
on Medical domain
SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation
In this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents.
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling
In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations.
