Results 11 to 20 of about 1,185,215 (298)
Bayesian Neural Word Embedding
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm.
Barkan, Oren, Oren Barkan
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An Enhanced Neural Word Embedding Model for Transfer Learning
Applied Sciences, 2022 Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However,
Md. Kowsher +6 more
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Sentiment-Aware Word Embedding for Emotion Classification
Applied Sciences, 2019 Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating
Xingliang Mao +4 more
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Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020 Word embeddings are a widely used set of natural language processing techniques that map words to vectors of real numbers. These vectors are used to improve the quality of generative and predictive models. Recent studies demonstrate that word embeddings contain and amplify biases present in data, such as stereotypes and prejudice.
Orestis Papakyriakopoulos +3 more
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Dynamic Contextualized Word Embeddings [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and ...
Hofmann, V +2 more
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Morphological Word-Embeddings [PDF]
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015 Published at NAACL ...
Ryan Cotterell, Hinrich Schütze
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Relational Word Embeddings [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding.
José Camacho-Collados +2 more
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Attention Word Embedding [PDF]
Proceedings of the 28th International Conference on Computational Linguistics, 2020 Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it.
Shashank Sonkar +2 more
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CoRR, 2021 This work presents a novel methodology for calculating the phonetic similarity between words taking motivation from the human perception of sounds. This metric is employed to learn a continuous vector embedding space that groups similar sounding words together and can be used for various downstream computational phonology tasks.
Rahul Sharma +2 more
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Dual embedding with input embedding and output embedding for better word representation [PDF]
, 2022 Recent studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms
Jihoon Lee +5 more
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