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There is no one-to-one correspondence between languages and their vocabularies. This means it is impossible for a computer translator to be invertible. The translator's task going from language A to B is fundamentally different from going from language B to A. To understand this, consider the French word "allumette", which in English is "match", that is, ...


19

from nltk.corpus import wordnet try: wordnet.synsets('test') except LookupError: import nltk nltk.download('wordnet') # For more information see: https://www.nltk.org/data.html def anti(word, fallback=None): for i in wordnet.synsets(word): for j in i.lemmas(): for k in j.antonyms(): return k.name() ...


18

The above answers are all good. I'd like to offer another perspective that I learned while teaching digital libraries that draws on the analogy used in biology: Computational biology = the study of biology using computational techniques. The goal is to learn new biology, knowledge about living sytems. It is about science. Bioinformatics = the ...


17

In English, one counterexample is the very common '-ed’ (often /d/) ending: ‘filled’ is 1 syllable, and the morphemes are ‘fill’ + ‘-ed’ (/d/).


14

In the realm of natural language, the "ideas a language can be used to express" are basically "any": all languages are capable of expressing any idea, so there's only one category of expressive type. Languages do differ in the way that they express a given idea. Assume a language Gwambomambo which lacks the word "recursion". That very word could be ...


12

The figure for entropy of any language will depend on the model we use for computing it. This is quite like how someone who speaks English well would see lesser entropy in English than someone who barely speaks the language. The model that Shannon used gave him a figure of 11 bits per word. Grignetti (1963) reported 9.83 bits per word. Some of the ...


10

Calculus can be a useful tool in quantitative linguistics. One simple example would be the deduction of the theoretical equation for the Piotrowski law modeling language change by Altmann et al. The increase of new forms p' is proportional to the product of the proportions p of new and 1-p old forms: p' ∝ p(1 - p). Introducing the factor of proportionality ...


10

Thanks for the comments on Heaps’ Law. I followed one of the links to a related law, Herdan’s Law. I’ll quote Wikipedia here: The rule is as follows: if V is the number of different words in the text, and n is the length of the text, then V will be proportional to n to the power β:         V ∝ nᵝ where β ranges from 0.5 to 1 depending on ...


10

By 'natural' you seem to be referring to what sounds (or phonemes) can be combined in what order. This is called phonotactics. For example, mo in your example mobify is a combination of a consonant and a vowel that fairly often occurs in English, in words such as motor (for simplicity I'm ignoring here that spelling doesn't exactly reflect pronunciation - ...


9

As with all natural laws, Zipf's law is an approximation. If you take a large corpus, and compute the Zipf curve, it will more or less follow a Zipf distribution (with coefficients thrown in to account for slack). This doesn't mean that for every language it follows the exact rule of 'the second most common lexical item is 1/2 as frequent as the most common'...


9

Dividing up the audio As you mentioned, formant analysis can place vowels nicely on a chart. But first you have to cut the vowels from the surrounding sounds. Often their formants are changed by nearby consonants; the nice F1/F2 plots use vowels in isolation, or the middle part of the vowel without the messy edges. And when vowels are reduced, or too ...


9

In computer science, one essential property of all Turing-complete languages is that they are able to describe, "in their own way", how they themselves work. For example, you can use a Turing machine to express how a Turing machine works. Similarly, you can write, for example, a Prolog program that can interpret Prolog programs. In the ...


8

Zipf’s law, as I understand it, is not really about languages, but about statistics and probability. It is just one of several formulations of the fact that many non-arbitrary sequences of numbers (frequency of words in a given corpus; population size of cites in relation to their rank; annual turnover of ranked companies; etc., etc.) are not evenly ...


8

I guess the NLTK documentation is a bit off. Looking at Wordnet's documents, I see: pos Syntactic category: n for noun files, v for verb files, a for adjective files, r for adverb files. And in another section of the same document: ss_type One character code indicating the synset type: n NOUN v VERB a ADJECTIVE ...


8

There are many spoken English corpora available. But generally, you need to ask more questions than 'plain text' before you find the right one. Length, level of annotation, format of annotation, type of conversation, genre/register, dialect, natural vs. elicited, etc. Those will all depend on the type of research questions you want to answer. If you just ...


8

You may want to look at D. Ringe's On Calculating the Factor of Chance in Language Comparison, which lays out some of the problems. I believe that uncontrolled variables are the greatest impediment to subjecting word-relatedness questions to valid statistical testing. Moreover, the idea that one could ever compute a p-value that a given word of a modern ...


8

No, natural languages aren't Turing complete in the same way onions are not. Quoting Wikipedia: A computational system that can compute every Turing-computable function is called Turing-complete (or Turing-powerful). A natural language is, very loosely speaking, a system of interpersonal communication among a group of people. It is not a computational ...


7

I happend to find this: EasyPronunciation.com Works okay for French, but it goofs up on some words, so watch out. Looks like English, Spanish and Chinese are also available there. Here's something for German: Donnerstag


7

The reason why trigrams can be considered powerful compared to n-grams of higher order, lies in the problem of data sparsity; when n is higher, data becomes increasingly sparse. Because of this, using trigrams is a good compromise which often yields good results. In a study by Chen and Goodman (1998), the effect of varying n-gram orders on the performance ...


7

The OP is making a very common mistake when it comes to comparing languages. If you can find a copy of Language Myths by Laurie Bauer and Peter Trudgill, I suggest you read Myth #2: Some Languages Just aren't Good Enough. If you can't find a copy then this blog should give you the rough idea. Let's examine the example given in the question: differentiate, ...


7

The paper, Non-projective Dependency Parsing using Spanning Tree Algorithms has a few examples of non-projective dependency trees. Note that dependency graphs are (1) not a formalism and (2) not standardized, so each researcher may re-define what happens in dependencies. Some of those dependency graphs are not even trees. Refer Generating Typed Dependency ...


7

This phenomenon is called zero copula. It especially common for third person present tense. I recommend that you read on how this is handled in syntax parsers for Russian or Hindi. It was also an issue for Irish, Hungarian, Japanese, Turkish, Arabic and many other languages.


7

There is the famous UPSID database: http://phonetics.linguistics.ucla.edu/sales/software.htm


7

I can only speak for Germany, and IANAL (I am not a lawyer). The situation is basically as follows: You can collect material from accessible sources (from the web, from radio broadcasts, from TV) and do analyses on that material You can do so within a closed collaboration with some collaborators including students and guests visiting your institution (they ...


7

I hadn't heard the term "statistical theory (of language)", but it seems to be a misnomer. I gather from your references that you take some data and use it to estimate the parameters of some statistical model. Model, not theory. We inherit our ideas about what empirical theories are like from the physical sciences, and a key property of those theories is ...


7

You say "... some critics say that these methods have not brought anything new ..." From my recollection of some old results (well outside my areas of expertise), I would say the problem is rather that automatic methods have not brought anything old. To have confidence in such a method, linguists would need to compare the classifications it comes up with ...


6

The other thing to keep in mind is that not only is IPA language-dependent, it's also convention dependent http://phonetic-blog.blogspot.co.uk/2012/09/false-alarm.html. It's really just a tool to help linguists communicate not a tool for exact representation of speech sounds.


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I think that Xophmeister's answer is pretty good. I wanted to chime in with the paper he or she was searching for, and since I don't have enough reputation to comment, I had to post an answer. In general, I would not exactly say that the P-NP problem is causing theoretical linguists to lose sleep. However, contingent on the conjecture that P does not equal ...


6

Imperative programming languages perform the instructions in the order you specify. Procedural languages (e.g. C) are imperative languages that allow you to group instructions into named blocks called functions or procedures. Object orientated languages like C++, Java and Python extend procedural languages with additional features. Prolog works in a ...


6

The treatment of English worked out in Generalized Phrase Structure Grammar by Gazdar, Klein, Pullum, & Sag is a CFG (with sets of rules given in highly abbreviated form) and is comprehensive in the sense that it includes the main areas of English covered in the TG literature. It is not a practical description, because there are too many rules, but it ...


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