Semantics 101 for Caterpillar Inc.

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It seems that the world’s largest manufacturer of construction equipment, Caterpillar Inc., is in serious need of a basic semantics lesson. I came across this article a couple days ago:

“Santa Cruz coffee shop with ‘cat’ in its name hit with cease and desist from Caterpillar Inc.”

Beyond the ridiculousness of a giant corporation going after a tiny local café, what struck me as even more absurd was the following:

  1. Even if the trademarked ‘CAT’ of Caterpillar Inc. was an oft-used clipping (shortening) of the full word ‘caterpillar’ (and so indicated that wriggling, butterfly-metamorphosing insect), it would not be the same word as the ‘cat’ of the café’s name – “Cat and Cloud Coffee” – which refers to the common feline house pet. These would be homonyms – words which are spelled alike, but have different meanings.[1]
  2. As it is, no one ever calls the aforementioned insect a ‘cat’ (not that I’ve heard, anyway). So the trademarked term is something else entirely. It has its own unique sense, which can in fact refer to at least two related things: (a) a particular machine produced by the company, or (b) the company itself. Obviously, neither of these are that purring, internet-beloved animal either. They are yet another set of homonyms.

Totally different words. Totally different senses. The news piece doesn’t say this explicitly, but most people possess an intuitive understanding, as evidenced by quotes from café customers:

“’I don’t think anyone correlates the Caterpillar company with their big yellow massive trucks with a small café,’ said Rick Tawfik, of San Jose. ‘I mean, I never thought about Cat and Cloud and Caterpillar in the same sentence until we heard about this lawsuit.’

‘I don’t think they have a legitimate case,’ added Emma Davis, of San Jose. ‘I don’t think I would ever confuse the two of them. It doesn’t make sense to me.’”

Caterpillar’s trademark lawyers apparently lack such common sense, or are (more likely) willfully ignoring it.

 

[1] Etymologically, hundreds of years ago, the terms could have been related, in that (according to the Oxford English Dictionary) the Middle English word for ‘caterpillar’ catyrpel may have derived from the Old French chatepelose (literally “hairy or downy cat”)…but enough time has elapsed between now and the 11th century that it’s not reasonable to claim a modern meaning connection. Does anyone you know think of caterpillars as “hairy cats”?

*Photo attributions: CAT excavator; Caterpillar; Pet cat

Pumpkin Pumpkin Pumpkin Pumpkin Pumpkin Pumpkin Pumpkin Pumpkin Pumpkin…

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Happy Halloween!

In tribute to the holiday (my favorite), here’s a smidge of spooky linguistics. Ever play that game where you repeat some word so many times it starts to lose its meaning? That’s actually a thing, called semantic satiation.

Semantic satiation is a psychological phenomenon where a person temporarily loses the meaning of the repeated word and perceives only nonsensical sounds. It can happen via reading as well as at the verbal/aural level. The term was coined by psychology professor Leon Jakobovits James in his 1962 dissertation[1]. His and later research shows that word repetition activates peripheral sensorimotor and central neural activity repeatedly in the cortex (activity corresponding with the meaning of a particular expression), which in turn causes reactive inhibition. Consequently, the strength of activity for each new repetition diminishes. More recent semantic verification studies have confirmed that this satiation legitimately falls under semantic memory, and is not just a byproduct of exhaustion of pre-semantic sensory/perceptual processes (for example, the acoustic system).[2]

That’s all well and good, but how is it spooky? In answer I say, see this movie – Pontypool.

In case you don’t have time to view it right this minute, my non-spoiler summary:

“Pontypool” is a low-budget psychological thriller that I found delightfully horrifying. The premise revolves around a virus that is infecting people in a small, remote Canadian town. The virus spreads…through language. When people hear an infected word, they begin repeating it until their entire speech grows garbled, they turn insane and zombie-like, and finally start attacking others in gory fashion.

I highly recommend leaving the office early today to go watch the film. Or to go trick-or-treating. Or something. Since everyone knows that all work and no play…

 

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[1] “Effects of repeated stimulation on cognitive aspects of behavior: some experiments on the phenomenon of semantic satiation”
[2] See “Introduction – Recent Studies” in “On the Locus of the Semantic Satiation Effect: Evidence from Event-Related Brain Potentials”

*Photo attributions: The Shining painting; Droste effect tv

Voynich: The manuscript that keeps on giving

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The Voynich manuscript is one of those marvels that, even in these times of boundless knowledge and incredible technology, eludes continual efforts to understand it.

Not heard of the thing? Welcome to the show. There has been a vigorous little dance of press coverage over the past couple years. It goes something like this:

Step to your left.  “An eternal mystery.”
Step to your right.  “I’ve cracked the code!” – some dude
Step back.  “Nope, you’re full of shit.”
Step forward.  “We’ve solved it this time for sure.” – some other dudes
Repeat.

The manuscript is a hand-written, illustrated codex that’s been shown through carbon dating to have originated in the early fifteenth century (1404–1438). The writing system used throughout its approximately 240 pages has yet to be identified.[1] Cryptographers, historians, computer scientists and others have proposed numerous hypotheses over the decades, including that it’s a hoax. Based on the illustrations, scholars divide the manuscript into five thematic sections: Herbal, Astrological, Biological, Pharmacological, and Recipes.

Below I list links to the (more recent) rhythmic pulse of “discoveries” and rejections, in chronological order. Under each link I’ve pulled out quotes of the more intriguing tidbits.

* * * * *

November 30, 2016: https://www.newyorker.com/books/page-turner/the-unsolvable-mysteries-of-the-voynich-manuscript

“The first half of the book is filled with drawings of plants; scholars call this the “herbal” section. None of the plants appear to be real, although they are made from the usual stuff (green leaves, roots, and so on […]). The next section contains circular diagrams of the kind often found in medieval zodiacal texts; scholars call this part “astrological,” which is generous. Next, the so-called “balneological” section shows “nude ladies,” in Clemens’s words, in pools of liquid, which are connected to one another via a strange system of tubular plumbing that often snakes around whole pages of text. […] Then we get what appear to be instructions in the practical use of those plants from the beginning of the book, followed by pages that look roughly like recipes.”

“The Voynich MS was an early attempt to construct an artificial or universal language of the a priori type.   –Friedman.”

* * * * *

September 8, 2017: https://arstechnica.com/science/2017/09/the-mysterious-voynich-manuscript-has-finally-been-decoded/

“Now, history researcher and television writer Nicholas Gibbs appears to have cracked the code, discovering that the book is actually a guide to women’s health that’s mostly plagiarized from other guides of the era.”

“Gibbs realized he was seeing a common form of medieval Latin abbreviations, often used in medical treatises about herbs. ‘From the herbarium incorporated into the Voynich manuscript, a standard pattern of abbreviations and ligatures emerged from each plant entry,’ he wrote. ‘The abbreviations correspond to the standard pattern of words used in the Herbarium Apuleius Platonicus – aq = aqua (water), dq = decoque / decoctio (decoction), con = confundo (mix), ris = radacis / radix (root), s aiij = seminis ana iij (3 grains each), etc.’ So this wasn’t a code at all; it was just shorthand. The text would have been very familiar to anyone at the time who was interested in medicine.”

“Gibbs concluded that it’s likely the Voynich Manuscript was a customized book, possibly created for one person, devoted mostly to women’s medicine.”

* * * * *

September 10, 2017: https://www.theatlantic.com/science/archive/2017/09/has-the-voynich-manuscript-really-been-solved/539310/

“This week, the venerable Times Literary Supplement published as its cover story a ‘solution’ for the Voynich manuscript. The article by Nicholas Gibbs suggests the manuscript is a medieval women’s-health manual copied from several older sources. And the cipher is no cipher at all, but simply abbreviations that, once decoded, turn out to be medicinal recipes.”

“’Frankly I’m a little surprised the TLS published it,’ says Lisa Fagin Davis, executive director of the Medieval Academy of America. When she was a doctoral student at Yale—whose Beinecke Library holds the Voynich manuscript—Davis read dozens of theories as part of her job. ‘If they had simply sent to it to the Beinecke Library, they would have rebutted it in a heartbeat,’ she says.”

“In the second part—only two paragraphs long—Gibbs gets into the meat of his solution: Each character in the manuscript is an abbreviated word, not a letter. This could be a breakthrough, but the TLS presents only two lines decoded using Gibbs’s method. Davis did not find those two lines convincing either. ‘They’re not grammatically correct. It doesn’t result in Latin that makes sense,’ she says.”

* * * * *

February 1, 2018: https://www.atlasobscura.com/articles/voynich-manuscript-artificial-intelligence-solved

“There are two problems with this notoriously difficult puzzle—it’s written in code, and no one knows what language that code enciphers.”

“’That was surprising,’ Kondrak said, in a statement. ‘And just saying “this is Hebrew” is the first step. The next step is how do we decipher it.’ The scientists think the code used in the manuscript might have been created using alphagrams. (In standard alphagrams, the letters in a word are placed in alphabetical order—the alphagram of ‘alphagram,’ for example, is ‘aaaghlpmr.’) Vowels also seemed to have been dropped. These assumptions made, they tried to come up with an algorithm to decipher this scrambled Hebrew text, to striking effect. ‘It turned out that over 80 percent of the words were in a Hebrew dictionary,’ said Kondrak.”

“Hebrew-speaking data scientist Shlomo Argamon offered some excoriating feedback. ‘They are saying it looks more like Hebrew than other languages,’ he said. ‘In my opinion, that’s not necessarily saying all that much.’ The use of Google Translate, too, struck him as somewhat unscientific. […] Other scholars have raised doubts about the scientists’ use of modern, rather than medieval, Hebrew.”

* * * * *

Certain researchers have made a compelling case against the “hoax” hypothesis, in any event. In 2013, an interesting paper analyzed the Voynich manuscript from an information theory perspective. They looked at organizational structure resulting from word distribution over the entire text, and concluded that there was “presence of a genuine linguistic structure”.[2] You can read the full paper here.

A couple information theory takeaways:

  1. Highly informative content words occur much more irregularly (and in clusters) throughout a text, while more uninformative function words tend to have a more homogenous or uniform distribution. So it’s the content words that indicate specific text sections.
  2. Words that are semantically related tend to co-occur in the same sections of a text.

 

Who will claim to have cracked the code next? My personal opinion, of course, is that they should throw some linguists on it.

 

[1] https://en.wikipedia.org/wiki/Voynich_manuscript

[2] Montemurro MA, Zanette DH. (2013). Keywords and Co-Occurrence Patterns in the Voynich Manuscript: An Information-Theoretic Analysis. PLoS ONE 8(6): e66344, 5. https://doi.org/10.1371/journal.pone.0066344

On machine translation, the media, and meaning (a response)

Shallowness_GoogTrans

I’m a Douglas Hofstadter fan. I read his book I Am a Strange Loop years ago, and it remains one of my three favorite non-fiction books, period. I highly recommend it to anyone who is at all interested in the nature of consciousness. The cognitive scientist’s Pulitzer Prize-winning Gödel, Escher, Bach: An Eternal Golden Braid has also been on my to-read list for a long time. So I was excited to see this article by him in The Atlantic, on another area that interests me: machine translation and machine “intelligence”.

Early on in the piece, Hofstadter says he has a “longstanding belief that it’s important to combat exaggerated claims about artificial intelligence”. Having worked in the machine learning/AI field for a little under a year now (but what an intense year it has been!), and having read countless popular media articles touting the astonishing advances in natural language processing/understanding, ML, and AI, I heartily agree with his sentiment. Such reporting is as misleading as it is annoying.

I came across a statement of this type the other day, in Stanford AI researchers make ‘socially inclusive’ NLP:

“The average person working with NLP today may consider language identification a solved problem.”

I have trouble believing that any researcher working in NLP/NLU/ML/AI thinks anything is a solved problem. Despite much progress, the field is still in its infancy. Doesn’t anyone remember Einstein’s quote (adapted from a similar idea expressed by Socrates) – “The more I learn, the more I realize how much I don’t know”? Where I work, every possible solution to a given problem brings up more questions, and even the “simplest” “facts” cannot always be taken for granted. (Remember when you were taught parts of speech like verb, noun, and preposition in grade school? Working at the level of detail we do, even these fundamental rules are often inadequate, requiring further specification. Turns out it’s hard to throw messy, real language into clean, fixed bins.) So I think the media does the field, its researchers, and the reading public a great disservice by sensationalizing and oversimplifying the challenges.

Hofstadter’s argument about understanding is even more poignant:

“The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. Machine translation has never focused on understanding language. Instead, the field has always tried to ‘decode’— to get away without worrying about what understanding and meaning are.”

We call the study of meaning and understanding semantics and pragmatics. People’s real world knowledge plays a key role here as well. To my mind, meaning (only complete when tied to real world knowledge) is the last frontier for AI and language. Today’s mobile/home voice assistants have definitely not yet mastered meaning. Technologies have made serious headway in resolving structural patterns (syntax), proper nouns (Named Entity Recognition) and some other aspects of language. But meaning, that great magical beast, eludes its pursuers. It is really, really challenging to computationally model the depth and complexity of human understanding. Because, although language itself is quite complicated, it’s still an impoverished medium for conveying the millions of subtle things we want and are able to convey – it relies heavily on context, implicature, presuppositionentailment, prosody, speaker-listener relationship, etc. I agree again with the author when he says that human-like machines are “not around the corner.”

I do think that Hofstadter seems to be simultaneously recognizing how hard the task of modeling meaning is, while not giving enough credit for things accomplished. Google Translate is way better now than it was at its inception over ten years ago. I also think he glosses over the tool’s main usage – which is more functional than artistic or poetic. Am I wrong to assume people use Translate much more for a quick word or phrase, when traveling or speaking on the fly, than for translating longer passages of literature? If they’re doing the latter… per the author’s experimental results, they clearly shouldn’t be.

 

What do you think – about media reportage of technology, machine “intelligence”, Hofstadter’s article? Feel free to comment!