Going Beyond Text: A Hybrid Image-Text Approach for Measuring Word Relatedness
By Chee Wee Leong, Rada Mihalcea. 2011
42 citations recorded by the source corpus.
Subfields: Natural Language Processing, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Mohit Bansal
- Douwe Kiela
- Christopher D. Manning
- Richard Socher
- Samuel R. Bowman
- Luke S. Zettlemoyer
- Yejin Choi
- Yoav Goldberg
In Representation & Self-Supervised Learning
- Marco Baroni
- Richard Socher
- Douwe Kiela
- Christopher D. Manning
- Luke S. Zettlemoyer
- Yoav Goldberg
- Samuel R. Bowman
- Angeliki Lazaridou
Citations in the corpus
- Zero-Shot Learning Through Cross-Modal Transfer (2013, background): “Some work has been done on multimodal distributional methods [11, 23].”
- Multimodal Distributional Semantics (2013, methodology): “The summing approach of Leong and Mihalcea (2011) corresponds to k=r, SL, β=0.5 (NaiveSL, below).”
- Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world (2014, background): “Very recently, a number of papers have exploited advances in automated feature extraction form images and videos to enrich context vectors with visual information (Bruni et al., 2014; Feng and Lapata, 2010; Leong and Mihalcea, 2011; Regneri et al., 2013; Silberer et al., 2013).”
- Multimodal Classification Fusion in Real-World Scenarios (2017, unknown): “compositionality and classification [2], [3], [4], [5], [6].”
- Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics (2014, background): “Recent works have shown that multi-modal semantic representation models outperform unimodal linguistic models on a variety of tasks, including modeling semantic relatedness and predicting compositionality (Feng and Lapata, 2010; Leong and Mihalcea, 2011; Bruni et al., 2012; Roller and Schulte im Wal”
- Language Grounding with 3D Objects (2021, background): “Joint embedding approaches that learn a shared subspace for representing language and vision tokens [26] also achieve state-of-the-art performance when trained at scale [1].”
- Distributional Semantics in Technicolor (2012, background): “Recently, NLPers have begun exploiting BoVW to enrich distributional models that represent word meaning with visual features automatically extracted from images (Feng and Lapata, 2010; Bruni et al., 2011; Leong and Mihalcea, 2011).”