Results 41 to 50 of about 19,032 (297)

Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning can They Capture? [PDF]

Sociological Methods and Research
Alina Arseniev-Koehler
exaly   +2 more sources

Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases

BMC Medical Informatics and Decision Making, 2018
Background In the past few years, neural word embeddings have been widely used in text mining. However, the vector representations of word embeddings mostly act as a black box in downstream applications using them, thereby limiting their interpretability.
Zhiwei Chen, Zhe He, Xiuwen Liu, Jiang Bian   +3 more
doaj   +1 more source

Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet.

International Journal of Interactive Multimedia and Artificial Intelligence, 2020
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, Poonam Sharma   +1 more
doaj   +1 more source

Acoustic Word Embeddings for End-to-End Speech Synthesis

Applied Sciences, 2021
The most recent end-to-end speech synthesis systems use phonemes as acoustic input tokens and ignore the information about which word the phonemes come from.
Feiyu Shen, Chenpeng Du, Kai Yu
doaj   +1 more source

Unveiling Biases in Word Embeddings: An Algorithmic Approach for Comparative Analysis Based on Alignment [PDF]

, 2023
openWord embeddings are state-of-the-art vectorial representation of words with the goal of preserving semantic similarity. They are the result of specific learning algorithms trained on usually large corpora.
SANGUIN, PIETRO MARIA
core  

Exploring the impact of word embeddings for disjoint semisupervised Spanish verb sense disambiguation

Inteligencia Artificial, 2018
This work explores the use of word embeddings as features for Spanish  verb sense disambiguation (VSD). This type of learning technique is named disjoint semisupervised learning: an unsupervised algorithm (i.e.
Cristian Cardellino, Laura Alonso Alemany   +1 more
doaj   +1 more source

Gender bias in Word Embeddings: toward a gender score analysis in textual documents. [PDF]

, 2022
openIl seguente elaborato proporne uno studio dei principali esperimenti condotti sui Word Embeddings, con un’attenzione particolare a quelli relativi ai WE nella lingua italiana e una seguente analisi del gender score nei documenti testuali.
FRISO, LUCA
core  

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

Word Embeddings: A Survey

CoRR, 2019
This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra ...
Felipe Almeida, Geraldo Xexéo
openaire   +2 more sources

Enhancing Accuracy of Semantic Relatedness Measurement by Word Single-Meaning Embeddings

IEEE Access, 2021
We propose a lightweight algorithm of learning word single-meaning embeddings (WSME), by exploring WordNet synsets and Doc2vec document embeddings, to enhance the accuracy of semantic relatedness measurement.
Xiaotao Li, Shujuan You, Wai Chen
doaj   +1 more source