Frame Semantics and FrameNet

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I’d like to discuss a theory in cognitive linguistics which is very near to my heart[1]: frame semantics. I’ll also present FrameNet, a database built using frame semantic theory, which has been and continues to be an excellent resource in the fields of natural language processing (NLP) and machine learning (ML).

Why is frame semantics cool? Why should you want to learn about it? Just this: the theory is an intuitive and comprehensive way to categorize the meaning of any scenario you could possibly dream up and express via language. Unlike many other semantic and syntactic theories, the core concepts are quickly understandable to the non-linguist. What’s more, frame semantics can apply to language meaning at many different levels (from the tiniest morpheme to entire swaths of discourse), and it works equally well for any particular language – be it English, Mandarin, Arabic, or Xhosa. I’ll try to demonstrate the theory’s accessibility and applicability with some details.

American linguist Charles Fillmore developed the frame semantics research program in the 1980s, using the central idea of a frame: a cognitive scene or situation which is based on a person’s prototypical understanding of real-world (social, cultural, biological) experiences. A frame is ‘evoked’ by language – this can be a single word (called a lexical unit), a clause, a sentence, or even longer discourse. Each frame contains various participants and props, called frame elements (FEs). If you’ve studied syntax/semantics (the generative grammar kind), FEs are somewhat analogous to traditional theta roles.

FrameNet is a corpus-based lexicographic and relational database (sort of a complex dictionary) of English frames, the lexical units evoking them, annotated sentences containing those lexical units, and a hierarchy of frame-to-frame relations. It was built and continues to grow at the International Computer Science Institute (ICSI), a nonprofit research center affiliated with UC Berkeley. FrameNets have also been developed in other languages, such as Spanish, Brazilian Portuguese, Japanese, Swedish, French, Chinese, Italian, and Hebrew.

Each frame entry includes a definition, example sentences, frame elements, lexical units, and annotation that illustrates the various fillers (words) of the FEs as well as their syntactic patterns. Let’s unpack all of this!

We’ll take a look at the Motion frame in FrameNet. Some screenshots of the frame entry follow.

framenet_motion1

The Motion frame is first defined. Its definition includes the frame elements that belong to the frame (the text with color highlighting):

“Some entity (Theme) starts out in one place (Source) and ends up in some other place (Goal), having covered some space between the two (Path). Alternatively, the Area or Direction in which the Theme moves or the Distance of the movement may be mentioned.”

After the definition come example sentences, featuring lexical units that evoke the frame (the black-backgrounded text) such as move, drift, float, roll, go.

Further down is the list of frame elements with their definitions and examples.

framenet_motion2

Here, the Theme FE is “the entity that changes location,” while the Goal FE is “the location the Theme ends up in.” In order for language to evoke this Motion frame, it must have some words or phrases which instantiate the Theme, the Goal, and the other FEs listed. In the examples above, me is a Theme in The explosion made [me] MOVE in a hurry; and into the slow lane is a Goal in The car MOVED [into the slow lane].

At the bottom of the entry is a list of lexical units that belong to or evoke the frame, as well as links to annotation of sentences from real data that contain those words.

framenet_motion3

Verbs like come, glide, roll, travel, and zigzag all evoke, quite sensibly, the Motion frame.

Once you click on the “Annotation” link for a particular lexical item, you’re taken to a page that looks like this:

framenet_motion4

Natural language sentences pulled from online corpora (texts from newspapers, magazines, books, tv transcripts, scholarly articles, etc.) are annotated for their Motion FEs. Annotation for the lexical item glide gives us an idea of the types of “entities” (the purple-backgrounded text, or Theme FEs) that “change location” (i.e. that glide) – boats, pink clouds, men, cars, planes, gondolas, and so on.

* * * * *

After this mini FrameNet dive, you may be wondering how the database is used in a concrete sense. To illustrate, let’s compare two sentences:

  1. The boat GLIDED into the harbor.
  2. The dingy DRIFTED away from the harbor.

The entities differ (boat vs. dingy), the verbs differ (glide vs. drift) and the prepositions differ (into vs. [away] from). Yet at a higher level, both of these sentences describe a Theme which “changes location” – either moving towards a Goal in (1), or from a Source in (2). They both indicate motion. Because FrameNet helps machines “learn” that sentences with a variety of nouns, verbs, prepositions, and syntactic patterns can basically point to the same scenario, it’s a useful tool for many applications in the computational realm.

These days computers do all kinds of language-y things for us: answer questions, paraphrase texts, extract relevant information from text (and then maybe organize it thematically – for instance, around people, places, or events), and even generate new texts. These feats require that a computer parse natural language into accurate semantic chunks. FrameNet’s semantically- and syntactically-annotated data can be used as training input for machine models that “learn” how to analyze such meaning chunks, enabling our electronic devices to respond, paraphrase, or extract information appropriately.

To peruse a (very long) list of the projects which have used FrameNet data (organized by requester/researcher), check out the FrameNet Downloaders page.

So – on the off-chance that you find yourself stuck at home and bored out of your mind (?!?!)… you might perhaps enjoy a little investigation of frame-semantic characterization of scenes that involve applying heat, intoxication, or temporal collocation. 🙂

 

[1] Why am I so fond of frame semantics? A terrific professor of mine during grad school introduced the theory, and it resonated with me immediately. I used it in my master’s thesis, then presented the paper at the International Conference on Construction Grammar in 2014. Eventually, I had the privilege of working at FrameNet, where I came to know the brilliant lexicographers/semanticists/cognitive linguists who have dedicated decades of their lives to the theory and the project. Sadly, I never met the legendary Chuck Fillmore, as he passed away the year before I joined the FrameNet team.

Career interviews: Linguistics Project Manager at a branding firm

working wugs_cropped

Wugs go to work

Something I’ve been planning to post occasionally are interviews with career linguists and related language folk – especially those working outside of academia. Yes, they (we) exist! Until recently these were rare birds, but lately the numbers are growing. I credit several factors: the growth of the discipline generally; the growth of technology industries trying to wrangle natural language; globalization; and (sadly), the increasing impracticality of landing a faculty position that pays a living wage, at least in the U.S.

Another (more popular!) language and linguistics blog has been running a job interview series over the last several years as well. I encourage you to also take a look over there: Superlinguo Linguist Job Interviews.

* * * * *

I met Noah on our team of linguists at Samsung Research America. For this interview, I asked him to talk about the job he had previous to Samsung – which was at Lexicon, a branding agency based in Northern California. Lexicon has come up with brand names for some of today’s most popular products, including Blackberry, Febreze, (Subaru) Outback, Dasani, Swiffer, Pentium, and ThermaCare.

noah

  1. What kind of work did you do at Lexicon?

I was a linguistics project manager, which basically meant that I coordinated with Lexicon’s network of linguists worldwide (85 countries with something like 50 or 60 languages represented). I basically sent them lists of names for real-time evaluation and also helped coordinate with another linguist in Quebec to prepare reports for deeper dives into particular names in order to ascertain particular issues a name might face in a target language or culture. Basically, you learn a lot of multilingual profanity doing this, and realize you shouldn’t name a company Zinda.

  1. Describe a typical day at that job.

It was a small company, so I wore whatever hats necessary. I prepared real-time and comprehensive reports, editing and working with the linguists to determine whether or not a given name that either a client had brought to us or one that we had brought would work well in a particular language, and also trying to read between the lines to figure out whether we should take a linguist’s comments at face value or do a little more digging and cross-checking, including interviews with native speakers. This was mostly done by our network, not in-house. But aside from the linguisty side, I also created names. Lots of names for lots of projects. Most of which didn’t actually make it through, but still it was a creative pursuit that stretched creativity. I also helped write a program to categorize and classify and try to ascertain good brand names using NLP [Natural Language Processing] techniques. Things like consonance, assonance, alliteration, etc. It was pretty helpful for going through our backlog of names and finding viable names to use going forward.

  1. How did your linguistics background inform that work?

Well, my fascination with language itself inherently got me the job and kept me entertained, though it would have easily, I think, been doable with some other kind of background. But creating a good name, actually looking into the science of sound symbolism, helping with a few linguistic studies. Pretty cool stuff.

  1. What did you enjoy most and/or least about the job?

I most enjoyed getting to see what kinds of things big clients were trying to market and create next. Some pretty cool things there, with an insider’s perspective as to what the market was going to look like in the future. Issues were managerial in nature, in combination with the claustrophobia that a small company can engender, but overall it was a very good way to get some experience in the field.

  1. What did you study in college and/or grad school?

Major: Linguistics. Minor: English. Minor: Business Administration (useless). Interest: everything else.

  1. What is your favorite linguistic phenomenon?

Splicing. Or whatever that thing was that we came up with as an inside joke that you should write a fake blogpost on.[1]

  1. (If you had the time) what language would you learn, and why?

ASL [American Sign Language]. As a monolingual, sign language has always fascinated me the most, oddly enough. Alas, those CSD [Communication Sciences and Disorders] students and their required classes.

  1. Do you have any advice for young people looking to pursue a career in linguistics?

Be overzealous, and marry linguistics to another discipline. In industry, you’ve got for the most part three choices: linguistics + [design, management, or computational]. Or astrobiology and linguistics if you happen to work with NASA. Might be cool. But yeah, linguistics is interdisciplinary by nature, so I assume everyone who studies it must enjoy the interplay of different subjects like I do. Oh, maybe start a computational linguistics club in undergrad when you don’t know anything about computational linguistics. It’ll make you learn, if nothing else.

 

 

[1] Noah sent me his responses long enough ago now that I cannot for the life of me remember what this was. Not that I would explain it even if I could remember, to preserve the opportunity of writing said fake blogpost. 😛

Semantics 101 for Caterpillar Inc.

catvscatvscat

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

I heart hangry bagel droids (or: How new words form)

The_fin_de_siècle_newspaper_proprietor_(cropped)

You’re probably familiar with the old adage “the only thing that’s constant is change.” Still, so many people tend to think about language as a relatively fixed affair. I’ve said it before (and will inevitably say it again): all living languages change all the time, and at all levels – phonological (sounds!), morphological (word-bits!), lexical (words!), syntactic (clauses!), and semantic (meaning!).

Historical linguistics (also known as diachronic linguistics) is the study of how and why languages change over time. In this post I’m going to discuss categories of change at the morphological and lexical levels – how new words come into being. In the future, I’ll explore semantic and perhaps phonological change.

Without further ado, here are the main mechanisms of word formation. Almost all examples are for English, but these formation types apply to other languages as well. (NOTE: Processes are not mutually exclusive. It is quite possible for a word to undergo multiple processes simultaneously, or one on the heels of another.)

  1. Derivation

New words are born by adding affixes to existing words. Affixes are bound[1] morphemes that can be prefixes, suffixes, and even (for certain languages, although not really for English) infixes and circumfixes. Derivation is a very common process cross-linguistically.

Zero derivation (also known as conversion) is a special case where a new word, with a new word class (part of speech) is created from an existing word of a different class, without any change in form.

Examples:
(Derivation) hater [hate + -er], truthiness [truth + -i (-y) + -ness], deglobalization [de- + globalization], hipsterdom [hipster + -dom]

(Zero derivation) heart as verb, as in “I heart coffee” [heart as noun]; friend as verb, as in “he friended me on Facebook” [friend as noun]; green as noun, in the golf lawn sense [green as adjective]; down as verb, as in “Hector downed a beer” [down as preposition]

  1. Back-formation

This process creates a new word through the removal of true or incorrectly assumed affixes. It’s kind of the opposite of derivation. This one is easier to explain through examples:

New word

Derived from older word

Analysis

donate, automate, resurrect

(verbs)

donation, automation, resurrection

(nouns)

The nouns were borrowed into English first from Latin. The verbs were back-formed later by discarding the -ion suffix, which speakers did through analogy with other Latinate verb and (-ion) noun pairs that already existed in English.

pea

pease

The older form was initially a mass noun (like water or sand), but was reanalyzed as plural. People then dropped the “plural” -s(e) to form the “singular” count noun pea.

beg, edit, hawk

(verbs)

beggar, editor, hawker

(nouns)

Speakers mistook the -ar, -or, and ­-er on the ends of these nouns (respectively) for the agentive suffix (that did/does exist in English), and removed it to form corresponding verbs.

lime-a-rita, mango-rita

appletini, kiwini

margarita

martini

Actually examples of folk etymology, which is related to back-formation. Here, speakers incorrectly assumed that -rita in margarita and –(t)ini in martini were separate morphemes (indicating the class of cocktail). Under that assumption, they switched out the rest of the word and substituted it with morphemes indicating new twists/ingredients.

  1. Blending

Also known as portmanteaus. Blends are produced by combining two or more words, where parts of one or both words are deleted.

Examples: smog [smoke + fog], brunch [breakfast + lunch], infomercial [information + commercial], bromance [bro + romance], hangry [hungry + angry], clopen [close + open][2]

  1. Borrowing

Also known as loan words. These are expressions taken from other languages. Pronunciation is usually altered to fit the phonological rules of the borrowing language.

Examples: algebra [from Arabic], ménage à trois [from French], whisky [from Scots Gaelic or Irish], bagel [from Yiddish], doppelgänger [from German], karaoke [from Japanese]

  1. Coinage

Words can be created outright to fit some purpose. Many of these are initially product names.

Examples: Xerox, Kleenex, Jell-O, Google, zipper, Frisbee

  1. Compounding

Two or more words join together to form a compound. Frequently the joining words are nouns, but they can belong to different parts of speech, including verbs, adjectives, prepositions, etc. Compounds can be separated by spaces, by hyphens, or glued to each other with nothing intervening.

Examples: homework, grocery store, mother-of-pearl, first world problem, binge-watch, weaksauce, fake news

  1. Eponyms

These are words that derive from proper nouns – usually people and place names. If a proper noun is used frequently enough and across multiple contexts, it eventually becomes a common noun (or verb or adjective).

Examples: sandwich [after the fourth Earl of Sandwich], gargantuan [after Gargantua, name of the giant in Rabelais’ novels], boycott [after Capt. Charles C. Boycott], mesmerize [a back-formation from mesmerism, in turn after Franz Anton Mesmer], sadism [after the Marquis de Sade]

  1. Reducing

Several types of reducing processes exist.  The main ones are clipping, acronyms, and initialisms.

a. Clipping

New words can be formed by shearing one or more syllables off an existing longer word. Syllables can be removed from the word’s beginning, end, or both.

Examples: fax [facsimile], flu [influenza], droid [android], fridge [refrigerator], blog [weblog]

b. Acronyms

Words are created from the initial letters of several other words. Acronyms are pronounced as regular words (in contrast to initialisms below).

Examples: NASA [National Aeronautics and Space Administration], RAM [random-access memory], FOMO [fear of missing out]

c. Initialisms

Also known as Alphabetisms. Like with acronyms, a word is created from the initial letters of other words, but the resulting term is pronounced by saying each letter. This usually happens when the string of letters is not easily pronounced as a word according to the phonological rules of the language.

Examples: NFL [National Football League], UCLA [University of California, Los Angeles], MRI [magnetic resonance imaging], WTF [what the fuck]

  1. Reduplication

Reduplication is one of my favorite phenomena.[3] It’s a process whereby a word or sound is repeated or nearly repeated to form a new word/expression. This is a productive morphological process (meaning, it’s part of the grammar and happens frequently and rather systematically) in many languages – South-East Asian and Austronesian languages particularly (e.g. Malay, Tagalog, Samoan). It’s not an especially productive process in English, although it does still happen.

Examples:
(English) wishy-washy, teensy-weensy, goody-goody, cray-cray, po-po

(Samoan) savali [‘he travels’ – third person singular + verb]; savavali [‘they travel’ – third person plural + verb]

* * * * *

Phew! Since hopefully you can see the light at the end of this long lexical tunnel, I’ll mention that of course languages lose words as well. Diverse factors motivate word loss, but that’s a subject for another post. A few quick examples of words that have fallen out of favor in English:

pell-mell [in a disorderly, reckless, hasty manner]; davenport [couch/sofa – my grandma used to say this]; grass [for marijuana – my mom still says this]; porridge [an oatmeal-like dish boiled in water or milk]; tumbrel [a farmer’s cart for hauling manure]; fain [gladly or willingly]

* * * * *

And now… ADD WORDS TO THE SPREADSHEET – Word shenanigans!

I’ve got almost 200 in there to start us off. If you’re not sure about the process for any particular word, just leave it blank or take a guess. Free bagel droids[4] to all who contribute.

 

[1] Bound meaning they cannot exist on their own, but must be attached to another morpheme.

[2] Describes a shitty situation where one has to work a closing shift followed by an opening shift. We used this term as bartenders, although I’d never seen it in print until recently. It came up in some paperwork I had to sign relating to work week ordinances, and then I saw it here as well.

[3] Some languages even have triplication – where the sound/word is copied twice!

[4] Kidding! These do not exist outside of my head. Sorry.