Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish [PDF]
Frontiers in Artificial Intelligence, 2022 Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the ...
Carolina Chiu +10 more
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Comparing general and specialized word embeddings for biomedical named entity recognition [PDF]
PeerJ Computer Science, 2021 Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used.
Rigo E. Ramos-Vargas +2 more
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Clustering and Visualising Documents using Word Embeddings
The Programming Historian, 2023 This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts.
Jonathan Reades, Jennie Williams
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Improving Word Embedding Using Variational Dropout
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings.
Zainab Albujasim +3 more
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Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information
Applied Sciences, 2022 The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and ...
Dongsuk Oh, Jungwoo Lim, Heuiseok Lim
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Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet
International Journal of Interactive Multimedia and Artificial Intelligence, 2021 Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words.
Krishna Siva Prasad Mudigonda +1 more
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Learned Text Representation for Amharic Information Retrieval and Natural Language Processing
Information, 2023 Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applications
Tilahun Yeshambel +2 more
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Natural language understanding of map navigation queries in Roman Urdu by joint entity and intent determination [PDF]
PeerJ Computer Science, 2021 Navigation based task-oriented dialogue systems provide users with a natural way of communicating with maps and navigation software. Natural language understanding (NLU) is the first step for a task-oriented dialogue system.
Javeria Hassan +2 more
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Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis
IEEE Access, 2021 Sentiment Analysis is an important research direction of natural language processing, and it is widely used in politics, news and other fields. Word embeddings play a significant role in sentiment analysis.
Yabing Wang +5 more
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Learning linear transformations between counting-based and prediction-based word embeddings. [PDF]
PLoS ONE, 2017 Despite the growing interest in prediction-based word embedding learning methods, it remains unclear as to how the vector spaces learnt by the prediction-based methods differ from that of the counting-based methods, or whether one can be transformed into
Danushka Bollegala +2 more
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