Results 41 to 50 of about 31,081 (304)
Bias Analysis in Word Embeddings with Alignment Techniques [PDF]
, 2023 openIn the field of Natural Language Processing, word embeddings are fundamental tools to represent the semantic relations among words. These tools are built by training learning algorithms on large corpora of textual data, which often reflect different ...
DELLA CASA, ELENA
core
Aspect Extraction of Case Microblog Based on Double Embedded Convolutional Neural Network [PDF]
Jisuanji kexue, 2021 Aspect extraction of the microblog involved in the case is a task in a specific domain.The expression of aspect words is diverse and the meaning is different from that of the general domain.Only relying on the word embedding in the general domain,these ...
WANG Xiao-han, TAN Chen-chen, XIANG Yan, YU Zheng-tao
doaj +1 more source
On the Dimensionality of Word Embedding
CoRR, 2018 In this paper, we provide a theoretical understanding of word embedding and its dimensionality. Motivated by the unitary-invariance of word embedding, we propose the Pairwise Inner Product (PIP) loss, a novel metric on the dissimilarity between word embeddings.
Zi Yin, Yuanyuan Shen
openaire +3 more sources
Word Embedding Techniques for Malware Classification
, 2020 Word embeddings are often used in natural language processing as a means to quantify relationships between words. More generally, these same word embedding techniques can be used to quantify relationships between features.
Chandak, Aniket
core +1 more source
Dual embedding with input embedding and output embedding for better word representation
, 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
core +1 more source
, 2021 These are several Arabic Word Embedding Models for NLP tasks and it has been described in our paper titled "Leveraging Arabic Sentiment Classification Using an Enhanced CNN-LSTM Approach and Effective Arabic Text ...
Abdulaziz Alayba
core +1 more source
Word Embedding With Zipf’s Context
IEEE Access, 2019 Word embeddings generated by neural language models have achieved great success in many NLP tasks. However, neural language models may be difficult to train and time consuming.
Lizheng Gao +3 more
doaj +1 more source
, 2015 Code for the ACL-2015 paper "Accurate Linear-Time Chinese Word Segmentation via Embedding ...
JM
core +1 more source
Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts
Frontiers in Big Data, 2020 Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing ...
Yu Meng +5 more
doaj +1 more source
Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification
Jurnal Riset Informatika, 2023 A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically.
Mukhamad Rizal Ilham, Arif Dwi Laksito
doaj +1 more source