Estimating the imageability of words by mining visual characteristics from crawled image data
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Hiroshi Murase | Daisuke Deguchi | Ichiro Ide | Takatsugu Hirayama | Yasutomo Kawanishi | Frank Nack | Marc A. Kastner
[1] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] L. Yee. Valence, arousal, familiarity, concreteness, and imageability ratings for 292 two-character Chinese nouns in Cantonese speakers in Hong Kong , 2017, PloS one.
[3] David A. Shamma,et al. YFCC100M , 2015, Commun. ACM.
[4] Christian Biemann,et al. What do we need to build explainable AI systems for the medical domain? , 2017, ArXiv.
[5] Adam Jatowt,et al. Estimating content concreteness for finding comprehensible documents , 2013, WSDM '13.
[6] Steven Bird,et al. NLTK: The Natural Language Toolkit , 2002, ACL.
[7] David M. Mimno,et al. Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets , 2018, NAACL.
[8] Hiroshi Murase,et al. Estimating the visual variety of concepts by referring to Web popularity , 2018, Multimedia Tools and Applications.
[9] Keiji Yanai,et al. Image region entropy: a measure of "visualness" of web images associated with one concept , 2005, MULTIMEDIA '05.
[10] Jamie Reilly,et al. Formal Distinctiveness of High- and Low-Imageability Nouns: Analyses and Theoretical Implications , 2007, Cogn. Sci..
[11] Kathy Hirsh-Pasek,et al. Imageability predicts the age of acquisition of verbs in Chinese children* , 2008, Journal of Child Language.
[12] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[13] Keiji Yanai,et al. Visual Analysis of Tag Co-occurrence on Nouns and Adjectives , 2013, MMM.
[14] Hanna Zijlstra,et al. Validiteit van de Nederlandse versie van de Linguistic Inquiry and Word Count (liwc) , 2005 .
[15] Veronika Coltheart,et al. Effects of word imageability and age of acquisition on children's reading , 1988 .
[16] Luc Van Gool,et al. Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..
[17] Qi Tian,et al. Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[18] P. Schwanenflugel. Why are Abstract Concepts Hard to Understand , 2013 .
[19] Ali Farhadi,et al. Learning Everything about Anything: Webly-Supervised Visual Concept Learning , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[20] Michael J Cortese,et al. Imageability ratings for 3,000 monosyllabic words , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.
[21] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[22] Chris Callison-Burch,et al. Learning Translations via Images with a Massively Multilingual Image Dataset , 2018, ACL.
[23] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[24] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[25] Steven Bird,et al. NLTK: The Natural Language Toolkit , 2002, ACL 2006.
[26] Anita Peti-Stantic,et al. Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings , 2018, Rep4NLP@ACL.
[27] Andreas Holzinger,et al. Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology , 2017, ArXiv.
[28] Koichi Shinoda,et al. Adaptation of Word Vectors using Tree Structure for Visual Semantics , 2016, ACM Multimedia.
[29] Barry Giesbrecht,et al. Separable effects of semantic priming and imageability on word processing in human cortex. , 2004, Cerebral cortex.
[30] Christian Wartena,et al. Predicting Word Concreteness and Imagery , 2019, IWCS.
[31] Meng Wang,et al. Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] A. Paivio,et al. Concreteness, imagery, and meaningfulness values for 925 nouns. , 1968, Journal of experimental psychology.
[33] Cordelia Schmid,et al. Evaluation of GIST descriptors for web-scale image search , 2009, CIVR '09.
[34] Gabriela Csurka,et al. Visual categorization with bags of keypoints , 2002, eccv 2004.
[35] Shuang Bai,et al. A survey on automatic image caption generation , 2018, Neurocomputing.
[36] Junyi Jessy Li,et al. Fast and Accurate Prediction of Sentence Specificity , 2015, AAAI.
[37] Gregory V. Jones. Deep dyslexia, imageability, and ease of predication , 1985, Brain and Language.
[38] Jinhui Tang,et al. Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains , 2016, ACM Trans. Multim. Comput. Commun. Appl..
[39] Mingda Zhang,et al. Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text , 2018, BMVC.
[40] Ton Dijkstra,et al. Affective Meaning, Concreteness, and Subjective Frequency Norms for Indonesian Words , 2016, Frontiers in psychology.
[41] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.
[42] Dorin Comaniciu,et al. Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[43] Klaus-Robert Müller,et al. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models , 2017, ArXiv.
[44] Keiji Yanai,et al. Automatic Construction of a Folksonomy-Based Visual Ontology , 2010, 2010 IEEE International Symposium on Multimedia.
[45] Michael Wilson. MRC Psycholinguistic Database , 2001 .
[46] Jinhui Tang,et al. Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation , 2015, ACM Multimedia.
[47] Michael S. Bernstein,et al. Empath: Understanding Topic Signals in Large-Scale Text , 2016, CHI.
[48] F. Smolík,et al. The power of imageability: How the acquisition of inflected forms is facilitated in highly imageable verbs and nouns in Czech children , 2015 .