Semantics

Compressed representations of reality for syntactic agents; which might be what meaning means



“ […] archetypes don’t exist; the body exists.
The belly inside is beautiful, because the baby grows there,
because your sweet cock, all bright and jolly, thrusts there,
and good, tasty food descends there,
and for this reason the cavern, the grotto, the tunnel
are beautiful and important, and the labyrinth, too,
which is made in the image of our wonderful intestines.
When somebody wants to invent something beautiful and important,
it has to come from there,
because you also came from there the day you were born,
because fertility always comes from inside a cavity,
where first something rots and then, lo and behold,
there’s a little man, a date, a baobab.

And high is better than low,
because if you have your head down, the blood goes to your brain,
because feet stink and hair doesn’t stink as much,
because it’s better to climb a tree and pick fruit
than end up underground, food for worms,
and because you rarely hurt yourself hitting something above
— you really have to be in an attic —
while you often hurt yourself falling.
That’s why up is angelic and down devilish.”

Umberto Eco. Foucault’s Pendulum.

On the mapping between linguistic tokens and what they denote.

If I had time I would learn about: Wierzbicka’s semantic primes, Wittgenstein, probably Mark Johnson if the over-egging doesn’t kill me. Logic-and-language philosophers, toy axiomatic worlds. Classic AI symbolic reasoning approaches. Drop in via game theory and neurolinguistics? Ignore most of it, mention plausible models based on statistical learnability.

Symbol grounding

Piantadosi and Hill (2022) on the Symbol grounding problem in transformers.

As a classification problem

Eliezer Yudkowsky’s essay, How an algorithm feels from the inside, which inspired Scott Alexander’s The Categories Were Made For Man, Not Man For The Categories.

From a different direction, Microsoft argues that objects are a kind of anchor point in training cross-modal AI systems. (Li et al. 2020)

…objects can be naturally used as anchor points to ease the learning of semantic alignments between images and texts. This discovery leads to a novel VLP framework that creates new state-of-the-art performance on six well-established vision-and-language tasks. …. Though the observed data varies among different channels (modalities), we hypothesize that important factors tend to be shared among multiple channels (for example, dogs can be described visually and verbally), capturing channel-invariant (or modality-invariant) factors at the semantic level. In vision-and-language tasks, salient objects in an image can be mostly detected by modern object detectors, and such objects are often mentioned in the paired text.

Also does embodiment means for this stuff, in terms of priors?

As an evolutionary phenomenon

Moved to Language games.

Simulacra

See simulacra.

Neurology of

What does the MRI tell us about denotation in the brain?

(Stolk et al. 2014) is worth it for the tagline: “experimental semiotics”

How can we understand each other during communicative interactions? An influential suggestion holds that communicators are primed by each other’s behaviors, with associative mechanisms automatically coordinating the production of communicative signals and the comprehension of their meanings. An alternative suggestion posits that mutual understanding requires shared conceptualizations of a signal’s use, i.e., “conceptual pacts” that are abstracted away from specific experiences. Both accounts predict coherent neural dynamics across communicators, aligned either to the occurrence of a signal or to the dynamics of conceptual pacts. Using coherence spectral-density analysis of cerebral activity simultaneously measured in pairs of communicators, this study shows that establishing mutual understanding of novel signals synchronizes cerebral dynamics across communicators’ right temporal lobes. This interpersonal cerebral coherence occurred only within pairs with a shared communicative history, and at temporal scales independent from signals’ occurrences. These findings favor the notion that meaning emerges from shared conceptualizations of a signal’s use.

Word vector models

Se vector embeddings.

References

Abend, Omri, and Ari Rappoport. 2017. The State of the Art in Semantic Representation.” In, 77–89. Association for Computational Linguistics.
Arbib, Michael. 2002. “The Mirror System, Imitation, and the Evolution of Language.” In Imitation in Animals and Artifacts, edited by Chrystopher Nehaniv and Kerstin Dautenhahn. MIT Press.
Baronchelli, Andrea, Tao Gong, Andrea Puglisi, and Vittorio Loreto. 2010. Modeling the emergence of universality in color naming patterns.” Proceedings of the National Academy of Sciences of the United States of America 107 (6): 2403–7.
Bengio, Yoshua, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A Neural Probabilistic Language Model.” Journal of Machine Learning Research 3 (Feb): 1137–55.
Bishop, J. Mark. 2021. Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It.” Frontiers in Psychology 11.
Cancho, Ramon Ferrer i, and Ricard V. Solé. 2003. Least Effort and the Origins of Scaling in Human Language.” Proceedings of the National Academy of Sciences 100 (3): 788–91.
Cao, Hui, George Hripcsak, and Marianthi Markatou. 2007. A statistical methodology for analyzing co-occurrence data from a large sample.” Journal of Biomedical Informatics 40 (3): 343–52.
Christiansen, Morten H, and Nick Chater. 2008. Language as Shaped by the Brain.” Behavioral and Brain Sciences 31: 489–509.
Corominas-Murtra, Bernat, and Ricard V. Solé. 2010. Universality of Zipf’s Law.” Physical Review E 82 (1): 011102.
Deerwester, Scott, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. 1990. Indexing by Latent Semantic Analysis.”
Elman, Jeffrey L. 1990. Finding Structure in Time.” Cognitive Science 14: 179–211.
———. 1993. Learning and Development in Neural Networks: The Importance of Starting Small.” Cognition 48: 71–99.
———. 1995. “Language as a Dynamical System,” 195.
Gärdenfors, Peter. 2014. Geometry of Meaning: Semantics Based on Conceptual Spaces. Cambridge, Massachusetts: The MIT Press.
Gozli, Davood. 2023. Principles of Categorization: A Synthesis.” Seeds of Science.
Guthrie, David, Ben Allison, Wei Liu, Louise Guthrie, and Yorick Wilks. 2006. A Closer Look at Skip-Gram Modelling.” In.
Kiros, Ryan, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2015. Skip-Thought Vectors.” arXiv:1506.06726 [Cs], June.
Lazaridou, Angeliki, Dat Tien Nguyen, Raffaella Bernardi, and Marco Baroni. 2015. Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation.” arXiv:1506.03500 [Cs], June.
Le, Quoc V., and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents.” In Proceedings of The 31st International Conference on Machine Learning, 1188–96.
Li, Xiujun, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, et al. 2020. Oscar: Object-Semantics Aligned Pre-Training for Vision-Language Tasks,” May.
Loreto, Vittorio, Animesh Mukherjee, and Francesca Tria. 2012. On the Origin of the Hierarchy of Color Names.” Proceedings of the National Academy of Sciences of the United States of America 109 (18): 6819–24.
Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space.” arXiv:1301.3781 [Cs], January.
Mikolov, Tomas, Quoc V. Le, and Ilya Sutskever. 2013. Exploiting Similarities Among Languages for Machine Translation.” arXiv:1309.4168 [Cs], September.
Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and Their Compositionality.” In arXiv:1310.4546 [Cs, Stat], 3111–19. Curran Associates, Inc.
Mikolov, Tomas, Wen-tau Yih, and Geoffrey Zweig. 2013. Linguistic Regularities in Continuous Space Word Representations. In HLT-NAACL, 746–51. Citeseer.
Narayanan, Annamalai, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. Graph2vec: Learning Distributed Representations of Graphs.” arXiv:1707.05005 [Cs], July.
Oscar: Objects Are the Secret Key to Link Between Language and Vision.” 2020. Microsoft Research (blog).
Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.” Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014) 12.
Petersson, Karl-Magnus, Vasiliki Folia, and Peter Hagoort. 2012. What Artificial Grammar Learning Reveals about the Neurobiology of Syntax.” Brain and Language, The Neurobiology of Syntax, 120 (2): 83–95.
Piantadosi, Steven T., and Felix Hill. 2022. Meaning Without Reference in Large Language Models.” arXiv.
Rizzolatti, Giacomo, and Laila Craighero. 2004. The Mirror-Neuron System.” Annual Review of Neuroscience 27: 169–92.
Smith, Kenny, and Simon Kirby. 2008. Cultural Evolution: Implications for Understanding the Human Language Faculty and Its Evolution.” Philosophical Transactions of the Royal Society B: Biological Sciences 363: 3591–3603.
Steyvers, Mark, and Joshua B. Tenenbaum. 2005. The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.” Cognitive Science 29 (1): 41–78.
Stolk, Arjen, Matthijs L. Noordzij, Lennart Verhagen, Inge Volman, Jan-Mathijs Schoffelen, Robert Oostenveld, Peter Hagoort, and Ivan Toni. 2014. Cerebral Coherence Between Communicators Marks the Emergence of Meaning.” Proceedings of the National Academy of Sciences 111 (51): 18183–88.
Zanette, Damiáan H. 2006. Zipf’s Law and the Creation of Musical Context.” Musicae Scientiae 10 (1): 3–18.

No comments yet. Why not leave one?

GitHub-flavored Markdown & a sane subset of HTML is supported.