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Word Embedding for Social Sciences: An Interdisciplinary Survey
v1v2 (latest)

Word Embedding for Social Sciences: An Interdisciplinary Survey

7 July 2022
Akira Matsui
Emilio Ferrara
ArXiv (abs)PDFHTML

Papers citing "Word Embedding for Social Sciences: An Interdisciplinary Survey"

15 / 15 papers shown
Title
Solving Cosine Similarity Underestimation between High Frequency Words
  by L2 Norm Discounting
Solving Cosine Similarity Underestimation between High Frequency Words by L2 Norm Discounting
Saeth Wannasuphoprasit
Yi Zhou
Danushka Bollegala
63
4
0
17 May 2023
Problems with Cosine as a Measure of Embedding Similarity for High
  Frequency Words
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
Kaitlyn Zhou
Kawin Ethayarajh
Dallas Card
Dan Jurafsky
75
68
0
10 May 2022
Network Representation Learning: From Preprocessing, Feature Extraction
  to Node Embedding
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
Jingya Zhou
Ling Liu
Wenqi Wei
Jianxi Fan
AI4TS
86
76
0
14 Oct 2021
Quantifying social organization and political polarization in online
  platforms
Quantifying social organization and political polarization in online platforms
Isaac Waller
Ashton Anderson
35
139
0
01 Oct 2020
Deep Learning for Generic Object Detection: A Survey
Deep Learning for Generic Object Detection: A Survey
Li Liu
Wanli Ouyang
Xiaogang Wang
Paul Fieguth
Jie Chen
Xinwang Liu
M. Pietikäinen
ObjDVLMOOD
162
2,455
0
06 Sep 2018
Learning Gender-Neutral Word Embeddings
Learning Gender-Neutral Word Embeddings
Jieyu Zhao
Yichao Zhou
Zeyu Li
Wei Wang
Kai-Wei Chang
FaML
96
415
0
29 Aug 2018
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
Nikhil Garg
L. Schiebinger
Dan Jurafsky
James Zou
AI4TS
69
965
0
22 Nov 2017
Multimodal Machine Learning: A Survey and Taxonomy
Multimodal Machine Learning: A Survey and Taxonomy
T. Baltrušaitis
Chaitanya Ahuja
Louis-Philippe Morency
101
2,932
0
26 May 2017
Poincaré Embeddings for Learning Hierarchical Representations
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel
Douwe Kiela
90
1,308
0
22 May 2017
Dynamic Word Embeddings for Evolving Semantic Discovery
Dynamic Word Embeddings for Evolving Semantic Discovery
Zijun Yao
Yifan Sun
Weicong Ding
Nikhil S. Rao
Hui Xiong
AI4TS
57
223
0
02 Mar 2017
Enriching Word Vectors with Subword Information
Enriching Word Vectors with Subword Information
Piotr Bojanowski
Edouard Grave
Armand Joulin
Tomas Mikolov
NAISSLVLM
229
9,972
0
15 Jul 2016
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
William L. Hamilton
J. Leskovec
Dan Jurafsky
93
923
0
30 May 2016
word2vec Explained: deriving Mikolov et al.'s negative-sampling
  word-embedding method
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Yoav Goldberg
Omer Levy
SSL
79
1,611
0
15 Feb 2014
Distributed Representations of Words and Phrases and their
  Compositionality
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov
Ilya Sutskever
Kai Chen
G. Corrado
J. Dean
NAIOCL
397
33,550
0
16 Oct 2013
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
677
31,512
0
16 Jan 2013
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