A Model of Grounded Language Acquisition: Sensorimotor Features Improve Lexical and Grammatical Learning.

By Steve R. Howell, Damian Jankowicz, Suzanna Becker. 2005

87 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Cultural Studies, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/4e679d0946d68663303278005b766fbdbc8f5649

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Mark Andrews
2. Peter Alexander Jansen
3. Marco Baroni
4. Mirella Lapata
5. Scott Watter
6. Carina Silberer
7. Gabriella Vigliocco
8. Marcus Rohrbach

In Cultural Studies

1. Mirella Lapata
2. Carina Silberer
3. Gabriella Vigliocco
4. Mark Andrews
5. Angeliki Lazaridou
6. Peter Alexander Jansen
7. Marco Baroni
8. Scott Watter

In Developmental and Educational Psychology

1. Mirella Lapata
2. Carina Silberer
3. Gabriella Vigliocco
4. Mark Andrews
5. Peter Alexander Jansen
6. Marco Baroni
7. David P. Vinson
8. Scott Watter

Citations in the corpus

• Grounded Models of Semantic Representation (2012, methodology): “A few models use feature norms as a proxy for sensorimotor experience (Howell et al., 2005; Andrews et al., 2009; Steyvers, 2010; Johns and Jones, 2012).”
• Grounding Action Descriptions in Videos (2013, background): “Psychological studies have shown the connection between action semantics and videos (Glenberg, 2002; Howell et al., 2005), but to our knowledge, we are the first to provide a suitable data source and to implement such a model.”
• Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing (2012, methodology): “The model might be modified to include modality-specific networks for the categories of perceptual features found in the Howell et al. (2005) input set, where the output of these networks would then converge upon the current integration area, creating – at least partially – an architecture similar to…”
• Combining Language and Vision with a Multimodal Skip-gram Model (2015, background): “Howell et al. (2005) propose an incremental multimodal model based on simple recurrent networks (Elman, 1990), focusing on grounding propagation from early-acquired concrete words to a larger vocabulary.”
• Integrating experiential and distributional data to learn semantic representations. (2009, background): “More relevant still is the work of Howell and Becker (2001); Howell, Becker, and Jankowicz (2001); and Howell, Jankowicz, and Becker (2005), who have shown that the acquisition of a language by a simple recurrent network is improved when words are augmented with sensory-motor representations.”