scholarly journals A Collection of Swedish Diachronic Word Embedding Models Trained on Historical Newspaper Data

2021 ◽  
Vol 7 ◽  
Author(s):  
Simon Hengchen ◽  
Nina Tahmasebi
Keyword(s):  
Word Embedding ◽  
Newspaper Data

Text Genre Detection Using Doc2Vec Word-embedding Language Model

2019 ◽  
Vol 23 (2) ◽  
pp. 23-43
Author(s):  
Dongsung Kim
Keyword(s):  
Language Model ◽  
Word Embedding ◽  
Text Genre

A Simple Word Embedding Model for Lexical Substitution

2015 ◽  
Author(s):  
Oren Melamud ◽  
Omer Levy ◽  
Ido Dagan
Keyword(s):  
Word Embedding ◽  
Lexical Substitution

Word Embedding Based Knowledge Representation with Extracting Relationship Between Scientific Terminologies

2019 ◽  
pp. -1--1
Author(s):  
Mucheol Kim ◽  
Junho Kim ◽  
Mincheol Shin
Keyword(s):  
Knowledge Representation ◽  
Word Embedding

Key phrase Extraction by Improving TextRank with an Integration of Word Embedding and Syntactic Information

2020 ◽  
Vol 13 ◽  
Author(s):  
Sheng Zhang ◽  
Qi Luo ◽  
Yukun Feng ◽  
Ke Ding ◽  
Daniela Gifu ◽  
...  
Keyword(s):  
Semantic Information ◽  
Performance Enhancement ◽  
Word Embedding ◽  
The Other ◽  
Test Set ◽  
Pagerank Algorithm ◽  
Phrase Extraction ◽  
Extraction Algorithm ◽  
Syntactic Information ◽  
Key Phrase Extraction

Background: As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm, which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis. Objective: The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key phrase extraction algorithm and achieved improvement. Method: In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm (WESIA), which improved the accuracy of the TextRank algorithm. Results: By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.

Assessing Suitable Word Embedding Model for Malay Language through Intrinsic Evaluation

2020 ◽  
Author(s):  
Yeong-Tsann Phua ◽  
Kwang-Hooi Yew ◽  
Oi-Mean Foong ◽  
Matthew Yok-Wooi Teow
Keyword(s):  
Word Embedding

A Custom Word Embedding Model for Clustering of Maintenance Records

2021 ◽  
pp. 1-1
Author(s):  
Abhijeet SANDEEP Bhardwaj ◽  
Akash Deep ◽  
Dharmaraj Veeramani ◽  
Shiyu Zhou
Keyword(s):  
Word Embedding

Enhanced Double-Carrier Word Embedding via Phonetics and Writing

2020 ◽  
Vol 19 (2) ◽  
pp. 1-18
Author(s):  
Wenhao Zhu ◽  
Xin Jin ◽  
Shuang Liu ◽  
Zhiguo Lu ◽  
Wu Zhang ◽  
...  
Keyword(s):  
Word Embedding

Cross-device matching approaches: word embedding and supervised learning

2021 ◽  
Author(s):  
Frank Yeong-Sung Lin ◽  
Chiu-Han Hsiao ◽  
Si-Yuan Zhang ◽  
Yi-Ping Rung ◽  
Yu-Xuan Chen
Keyword(s):  
Supervised Learning ◽  
Word Embedding

Improving word embedding quality with innovative automated approaches to hyperparameters

2021 ◽  
Author(s):  
Beytullah Yildiz ◽  
Murat Tezgider
Keyword(s):  
Word Embedding
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