Commerce is a human convention deeply entwined with language. Economic motivations were among the many reasons ancient (and modern) empires conquered other lands, spreading their languages beyond their natural range. Traders would travel to distant lands, encountering speakers of exotic languages. And where two languages meet, words begin to exchange back and forth. In cases where bilingual speakers were few to none, Pidgin languages developed. Pidgins are languages with simplified grammar and vocabulary, and are never spoken as a first language. They come about as a means of communicating between speakers of different languages for the purpose of trade. When a Pidgin is spoken widely enough that children in the community grow up learning it as a first language, the language changes into a Creole. Creoles have many fascinating characteristics, but the point here is, commerce is a driving factor in their creation. When a conquering empire brings its own language, it either supplants the native language or influences it heavily. Pidgins, on the other hand, develop because speakers are motivated to communicate in order to trade.

Groups of speakers who remain in constant contact tend to speak the same dialect of a language. When a group breaks off and becomes isolated (contact with the original group is infrequent or not widespread), their dialects begin to diverge. Mass communication is changing this landscape, allowing larger and larger people groups to remain in constant contact. As a result, minority languages are being spoken even less in favor of popular languages. This process is called linguistic homogenization. If we follow the slippery slope to the extreme, eventually there will be a single language spoken by all people. This eventuality isn’t likely to happen in our lifetimes, and not just because it requires almost all native speakers of a language to die out. A far more likely scenario is that a handful of commerce languages will be spoken by the vast majority of people. Commerce languages are popular languages people speak to do business in (English, Mandarin, etc).

There are many factors driving linguistic homogenization. Commerce is certainly one of them. In the modern world of the internet and mass media, attention is the scarce resource people are competing for. If you want to capture the attention of others, you need to maximize your reach and doing so typically means choosing a language of commerce. Minority languages present a barrier to the widest possible dissemination of information (except when the only intended audience are speakers of that language). The attention economy promotes linguistic homogenization.

Machine translation services, such as Google Translate, potentially have the power to change this. As the quality of these services improve, it becomes less and less necessary to publish exclusively in commerce languages. Linguistic homogenization may not be the inexorable force it appears to be today. Of course, the output of machine translation can be pretty abysmal. Will the quality of machine translation improve fast enough, and will the business case for them be strong enough to turn the tide of linguistic homogenization?  Those betting on machine translation services surely hope so. But there is a dueling problem here. In order for machine translation to truly counteract linguistic homogenization, it has to be freely available (or ridiculously cheap). These systems are difficult to build and require great computational resources. The outcome will almost certainly be a matter of economics as well as science.

While the future progress of commerce and language may be uncertain, what is certain is that they will continue to heavily influence each other. And there’s nothing new about that.

What would it even mean for a word to lose its meaning? A word can change from one meaning to another, certainly.  Maybe you could argue that a word that has dropped out of usage has lost its meaning..

You hear complaints of that sort all the time, but what is being missed is the fact that language is fluid. Meanings evolve as the need arises (and there are many kinds of  needs). Speakers each carry a somewhat different representation of the language in their heads, and once like-minded speakers agree on a novel usage and adapt it into their own representations, language evolves.

The debate over literally is literally nothing new. Turning to old faithful, the American Heritage dictionary:

Usage Note: For more than a hundred years, critics have remarked on the incoherency of using literally in a way that suggests the exact opposite of its primary sense of “in a manner that accords with the literal sense of the words.” In 1926, for example, H.W. Fowler cited the example “The 300,000 Unionists … will be literally thrown to the wolves.” The practice does not stem from a change in the meaning of literally itself—if it did, the word would long since have come to mean “virtually” or “figuratively”—but from a natural tendency to use the word as a general intensive, as in They had literally no help from the government on the project, where no contrast with the figurative sense of the words is intended.

So literally has been known to be a general intensive for quite some time. Why the fuss now?

Twitter is my new linguistic data collection engine, btw.  Just some of the multitude of great results:

• ipodrulz: My dog is whining because I’m keeping her up! She hates it when she’s asleep and I’m not… fucking bitch – literally.
  14 minutes ago · Reply · View Tweet
• justinpeacock: Just finished up a great songwriting session with @davehedin and a most talented artist. She is literally exploding with creativity. Hol …
  16 minutes ago · Reply · View Tweet
• prasannathani: @int23 omg… I’m soo sorry… speechless, literally.
  18 minutes ago · Reply · View Tweet · Show ConversationHide Conversation
• himanshuc: RT @anupkaphle: Here’s a BBC video on my dark country. I mean literally dark: http://is.gd/huEx (expand)
  19 minutes ago · Reply · View Tweet
• anupkaphle: Here’s a BBC video on my dark country. I mean literally dark: http://is.gd/huEx (expand)
  22 minutes ago · Reply · View Tweet
• jandthecity: ugh this new knitting project has been restarted literally 11 times. :( I hope it turns out the way I want it to!!
  27 minutes ago · Reply · View Tweet
• TurboFool: The WTF Blanket: http://is.gd/hhGX- (expand) I was literally laughing out loud by the end of this one.
  27 minutes ago · Reply · View Tweet
• Charpie: Holy shit. The Fort is on fire! Not literally. I know you fuckers think i’m a pyro.
  27 minutes ago · Reply · View Tweet

References

Dictionary.com, “literally,” in The American Heritage® Dictionary of the English Language, Fourth Edition. Source location: Houghton Mifflin Company, 2004. http://dictionary.reference.com/browse/literally. Available: http://dictionary.reference.com. Accessed: January 27, 2009.

So I was listening to a promo video (below) by Richard Branson of Virgin Galactic.  Branson opens up with this line:

And quite frankly, the sinister music blends with my interpretation of enormity far better.  Astronauts have all returned overwhelmed by the vast wickedness they encountered in space.  Awesome!  I totally wanna go now.  Actually, I’ve always wanted to go and probably would go even if I was told I had a 50/50 chance of making it back alive, so enormity just ups the thrill level.

Simple String Matching

Simple string matching involves matching identical substrings in the word pair. Throughout this document, word pair will mean the two words in separate languages being considered to determine their status as cognates. Simard et al. (1993) used a measure of cognateness based on matching the first four letters in each word. They used this cognateness measure to align sentences in a bilingual corpus (the Hansards). Their hypothesis was that there would be more cognates in aligned substrings than in non-aligned. They hand aligned a portion of the Hansards and found that there was a statistically significantly higher number of cognates in aligned parts.

Dice’s Coefficient

Dice’s coefficient is another string similarity measure that has been used for cognate identification. Given two aligned hypotheses, Dice’s coefficient is calculated as

where f_x,y is the co-occurrence of items in the aligned set and f_x and f_y are the two sets under consideration. As with any string similarity score, pairs of strings that are rated as more similar can be considered to be cognates.

Longest Common Subsequence Ratio

The Longest Common Subsequence Ratio (LCSR) is another measure of string similarity that takes advantage of the observation that parts of a string may be similar while the prefixes and suffixes are not (or any other part of the string). The LCSR is computed by finding the longest substring in common between the two strings and returning the ratio of the length of that string to the length of the longest of the two words in the pair. This then becomes a measure of the two words’ cognateness.

Tiedemann’s Three String Similarity Measures

Joerg Tiedemann (1999) developed three string similarity measures that lie at the boundary of what I consider orthographic methods. Whereas other string similarity measures make no distinction between the individual characteristics of letters, Tiedemann’s measures do. In particular, he makes a distinction between vowels and consonants and the distributions that generate them.

His algorithms rely on a dynamic programming technique from Stephen (1992) to compute the LCS. A matrix is created with n rows and m columns, where n is the length of the first string and m is the length of the second. Beginning at position 0 (before the first character in both strings), a string matching function is applied to each character pair for each cell in the table.  For example, this function could return 1 if the characters match according to the function and 0 if they do not. The value for each cell is computed as follows (Tiedemann, 1999):

The final cell contains the LCS for these two strings if the matching function used is the example (match=1/mismatch=0) above.  Tiedemann recognized that another matching function could be used.  Since we are no longer computing the longest common subsequence, he chose the term highest score of correspondence (HSC).

Tiedemann describes three matching functions:  VCchar, VCseq, and NMmap, where V=vowel, C=consonant, and NM=non-matching.   I refer the reader to the paper (pdf) for details on each of these, but I will touch on certain aspects briefly.  In VCchar, pairs of vowels and consonants that occur together more frequently are given more weight in the matching function.  If they never co-occur, their value is 0.  Now remember we are talking about one character from each language.  Tiedemann found interesting mappings with this, like English c for Swedish k.  He also found that there were spurious results and so requires tuning of a threshold function.  VCseq is similar to VCchar but instead he looks at longer sequences of characters rather than single ones.  NMmap takes a different approach.  Instead of matching similar characters, it looks at parts of strings that are not the same.  I personally found this to be the most interesting and it produced neat mappings like Swedish ska to English c (e.g. Swedish automatiska to English automatic).

References

M. Simard, G.F. Foster, and P. Isabelle. Using cognates to align sentences in bilingual corpora. In Proceedings of the 1993 Conference of the Centre for Advanced Studies on Collaborative research: Distributed Computing-Volume 2, pages 1071-82, 1993.

Stephen, Graham A. String Search. Technical Report TR-92-gas-01, School of Electronic Engineering Science, University College of North Wales, 1992.

Tiedemann, Joerg. Automatic construction of weighted string similarity measures. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999.

So a good question might be why does the data being used matter so much to these techniques? Why not classify the two approaches as to whether they look for loose or strict cognates? Might there not be another way of classifying the approaches to cognate identification beyond these two? Or is there an entirely different set of classes that would better describe them? To answer the last two questions, I will say that there very well may be better ways of classifying these algorithms. As Anil pointed out in the comments to my last post, the two definitions lend themselves to different applications. From the papers that I read, it seemed that when researchers looked at plain text data, there was a completely different mindset than in papers where researchers used phonetic transcriptions. For the former, the goal was usually finding translational equivalences in bitext and for the latter the goal is more as an aid to linguists attempting to reconstruct dead languages or establish relationships between languages.

With plain text, it is very difficult to infer sound correspondences between two languages. In Old English, the orthography developed by scribes corresponded directly to the spoken form. As English changed over the 1000+ years since then, the orthographic forms of words have frozen in some cases and not in others. For example, the word knight was originally spelled cniht and the c and h were both pronounced. The divergence of orthographic and phonetic forms can result in any number of problems and so it influences the ways of thinking about the task. On the other hand, phonetic approaches suffer due to data scarcity. Obtaining phonetic transcriptions is expensive as it requires the effort of linguists or individuals with specific, extensive training in the area. There are ways of obtaining phonetic transcriptions automatically, but these methods are not perfect and so result in noisy data, making this data practically useless for historical linguists.

In my next post, I will go into orthographic approaches in more detail, describing some of the papers I looked at and the methods they used. After that, I will begin discussing phonetic approaches, which are more numerous. I will also begin to look at how machine learning is being used to tackle cognate identification.

View all posts on cognate identification.

First of all, to frame the problem, historical linguistics is a branch of linguistics that studies language change. Language can change in many ways, but the methods we looked at pretty much solely focused on phonological and semantic changes, with a few brief nods to syntactic change (on the phylogenetic inference side). The main tool used by historical linguists in reconstructing dead languages is the comparative method. This method looks at two languages suspected of being related and tries to infer the regular sound changes that led to the divergence. By examining lists of suspected cognates, they find sound correspondences — sounds that appear in similar contexts in both languages, but which aren’t necessarily the same phoneme. For example, the word for beaver in English and German derives from the Proto-Germanic word *bebru. In Old English, this became beofor (the f sounds like a /v/). In modern German, the word is Biber, with the /b/ phoneme preserved as it was in Proto-Germanic. So we could infer a sound correspondence between English /v/ and German /b/ in this context.

So what are cognates? If you have studied a second language, you no doubt have heard this term. I propose the following two classifications for cognates. A loose cognate will be a pair of words in two languages that is spelled or pronounced the same, with some minor variations. In this way, French resumé and English resumé would be considered cognates. Loose cognates have also been called orthographic cognates. A strict cognate is a pair of words in two related languages that descended from the same word in the ancestor language. Loan words are words that come into a language directly from another language, such as resumé. These words do not undergo the regular sound changes that are observed in strict cognates and so they are not considered cognates at all by historical linguists.

What is the effect the distinction between these two definitions would have on computational approaches to this task? I will look at this further in a future post, but feel free to post your thoughts in the comments.

Another interesting example of a similar nature is home in on versus the original hone in on.

1. Goods of any sort that are white in color — flour, paper towels, lilies, emulsion paint, toothpaste, ermine fur, milk, eggs, refined sugar, button mushrooms, etc.
2. Goods that carry no duty and can thus be freely imported and carried through customs without officials needing to be in any way concerned with them.
3. Garments typically or traditionally made with undyed white cotton, such as plain dress shirts, underwear, tennis shorts, cricket clothes, and so on.
4. Goods that are fully legal, in the sense of being properly imported with duties properly paid rather than being part of the so-called “black economy”.
5. Office paper, letter envelopes, and similar white paper office supplies.
6. Household appliances such as washing machines or refrigerators that are often painted white.
7. Linen household goods such as sheets, pillow cases, and towels.
8. Goods of a sort determined by market research to be primarily of interest to customers of European rather than African or Asian origins.
9. Goods deemed by government regulatory agencies to be (unlike an increasing number of toys and other products from the People’s Republic of China) free of harmful features and fully fit for sale to the general public.
10. Milk, buttermilk, yoghurt, and other non-cheese liquid dairy products.

The correct answer will be posted on Language Log in a couple days. I’ll post an update here as well.  Answer is under the fold.

Update

Well the answer was posted on language log sooner than I thought, so I’ll just post it here under the fold.   The answer is #6:  “Household appliances such as washing machines or refrigerators that are often painted white.”  My guess was #4.