Results 51 to 60 of about 256,478 (282)
, 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.
Sharma, Rahul +2 more
openaire +2 more sources
Closed Form Word Embedding Alignment [PDF]
2019 IEEE International Conference on Data Mining (ICDM), 2019 We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two ...
Sunipa Dev +2 more
openaire +2 more sources
A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
Applied Sciences, 2022 Since some sentiment words have similar syntactic and semantic features in the corpus, existing pre-trained word embeddings always perform poorly in sentiment analysis tasks.
Qizhi Li +4 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio +8 more
wiley +1 more source
Future Internet, 2018 Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel ...
Yue Wu, Junyi Zhang
doaj +1 more source
A Smaller and Better Word Embedding for Neural Machine Translation
IEEE Access, 2023 Word embeddings play an important role in Neural Machine Translation (NMT). However, it still has a series of problems such as ignoring the prior knowledge of the association between words, relying on specific task constraints passively in parameter ...
Qi Chen
doaj +1 more source
Quantum-Inspired Complex Word Embedding [PDF]
Proceedings of The Third Workshop on Representation Learning for NLP, 2018 A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words. For example, existing approaches in word embeddings will assign high probabilities to the words "Penguin" and "Fly" if they frequently co-occur, but it fails to capture the fact that they occur in an opposite sense - Penguins do ...
Li, Qiuchi +3 more
openaire +2 more sources
Arthritis Care &Research, EarlyView.Objective To support high‐quality, patient‐centered care for systemic lupus erythematosus (SLE), the American College of Rheumatology (ACR) developed evidence‐based measures incorporating clinical and patient‐reported outcome measures (PROMs). Using the Consolidated Framework for Implementation Research (CFIR), we conducted semistructured interviews ...
Catherine Nasrallah +13 more
wiley +1 more source
Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings
, 2019 Word embeddings have gained significant attention as learnable representations of semantic relations between words, and have been shown to improve upon the results of traditional word representations.
Devlin Jacob +10 more
core +1 more source
Measuring Societal Biases in Text Corpora via First-Order Co-occurrence
, 2020 Text corpora are used to study societal biases, typically through statistical models such as word embeddings. The bias of a word towards a concept is typically estimated using vectors similarity, measuring whether the word and concept words share other ...
Hanbury, Allan +3 more
core +2 more sources