Results 21 to 30 of about 1,006,138 (295)
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
openaire +3 more sources
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
doaj +1 more source
Socialized Word Embeddings [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Word embeddings have attracted a lot of attention. On social media, each user’s language use can be significantly affected by the user’s friends. In this paper, we propose a socialized word embedding algorithm which can consider both user’s personal characteristics of language use and the user’s social relationship on social media.
Ziqian Zeng +3 more
openaire +1 more source
Slovene and Croatian word embeddings in terms of gender occupational analogies
Slovenščina 2.0: Empirične, aplikativne in interdisciplinarne raziskave, 2021 In recent years, the use of deep neural networks and dense vector embeddings for text representation have led to excellent results in the field of computational understanding of natural language.
Matej Ulčar +3 more
doaj +1 more source
Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning Can They Capture? [PDF]
Sociological Methods and ResearchAlina Arseniev-Koehler
exaly +2 more sources
Cultural Cartography with Word Embeddings [PDF]
Poetics, 2020 Using the frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized and continuous “meaning-space” where words are assigned a location based on relations of similarity to other words ...
Stoltz, Dustin, Taylor, Marshall
openaire +4 more sources
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
doaj +1 more source
Decoding Word Embeddings with Brain-Based Semantic Features [PDF]
, 2021 Word embeddings are vectorial semantic representations built with either counting or predicting techniques aimed at capturing shades of meaning from word co-occurrences.
Emmanuele Chersoni +7 more
core +1 more source
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
doaj +1 more source
A Collection of Swedish Diachronic Word Embedding Models Trained on Historical Newspaper Data
Journal of Open Humanities Data, 2021 This paper describes the creation of several word embedding models based on a large collection of diachronic Swedish newspaper material available through Språkbanken Text, the Swedish language bank.
Simon Hengchen, Nina Tahmasebi
doaj +1 more source