Results 71 to 80 of about 14,560 (254)
A metaheuristic with a neural surrogate function for Word Sense Disambiguation
Machine Learning with Applications, 2022 Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context.
Azim Keshavarzian Nodehi +1 more
doaj
Multi-Head Self-Attention Gated-Dilated Convolutional Neural Network for Word Sense Disambiguation
IEEE Access, 2023 Word sense disambiguation (WSD) is to determine correct sense of ambiguous word based on its context. WSD is widely used in text classification, machine translation and information retrieval and so on.
Chun-Xiang Zhang +2 more
doaj +1 more source
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge [PDF]
Proceedings of the 11th International Workshop on Semantic
Evaluation (SemEval-2017), p. 85-89, 2017 This paper describes Luminoso's participation in SemEval 2017 Task 2, "Multilingual and Cross-lingual Semantic Word Similarity", with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemEval was an update of previous work
arxiv +1 more source
A Unified multilingual semantic representation of concepts [PDF]
, 2015 Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel
CAMACHO COLLADOS, Jose' +2 more
core +2 more sources
SemEval-2020 Task 4: Commonsense Validation and Explanation [PDF]
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020 In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons.
Yilong Wang +5 more
openaire +3 more sources
IEEE Access, 2023 With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the
Kia Jahanbin, Mohammad Ali Zare Chahooki
doaj +1 more source
SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis [PDF]
arXiv, 2022 We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular multilingual pre-trained language models. The dataset is released along with the SemEval 2023 Task 9: Multilingual Tweet
arxiv
IEEE AccessTarget Aspect Sentiment Detection (TASD) is challenging because it involves various Natural Language Processing (NLP) subtasks including opinion target detection and sentiment polarity classification.
Mohammad Radi, Nazlia Omar, Wandeep Kaur
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Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text
IEEE AccessAspect extraction poses a significant challenge in Natural Language Processing (NLP). Extracting explicit and implicit aspects from online text data remains an ongoing challenge despite significant research efforts.
Mikail Muhammad Azman Busst +4 more
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ADeCNN: An Improved Model for Aspect-Level Sentiment Analysis Based on Deformable CNN and Attention
IEEE Access, 2020 Aspect-level sentiment analysis aims at identifying the sentiment polarity of target in the context. In most of the previous sentiment analysis models, there usually exists the problem of insufficient extraction capability of local features and long ...
Jie Zhou, Siqi Jin, Xinli Huang
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