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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() ...


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 ...


13

No, you do not have a point, because (good) science does use words accurately and unambiguously. But it is probably true that they don't use the words that you would prefer, or assign the definitions that you would prefer. I suspect that you would not get it if I tried to teach you about phonology, because I use a number of special words and specially-...


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

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

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 ...


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 ...


9

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

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

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

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 ...


8

For English there exists a list of Basic OCR corrections by Ted Underwood and Loretta Auvil. In the linked blog they also explain how they generated that list of corrections by simulating typical errors automatically. We improved on that for the Royal Society Corpus and our scripts to do that are available for download here. Our approach is tuned for ...


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

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 ...


6

The major dichotomy in NLP is that of rules-based approaches vs statistical approaches. (Machine learning is used in some statistical approaches. Given the timeframe of the development of the field, the earliest statistical approaches, for example those of SMT pioneers, surely can or will be seen as classical.) Rules-based approaches are basically the ...


6

"Computational linguistics is trying to teach computers to understand ordinary language" would probably make sense to most laypeople. The problem, of course, is that if you tell people something they actually understand, they may respond "Oh, wow! How do you do that?"—and you're back where you started.


6

The most basic problem is that it is impossible (given any realistic i.e. non-Star Trek technology) to map waveforms to IPA letters for an arbitrary language. It is, however, possible for well-enough studied languages, using Google-grade technology, for example you can speak Norwegian or English to Google, it will return the spelling, and you can use that to ...


6

Contrary to the expectations of some commentators, doctor-patient corpora are available (under some conditions, needing to sign some licence and confidentially agreement) for research. The standard entry point for a search for such corpora is the CLARIN Virtual Language Observatory and entering doctor patient in the search slit gives currently twelve results....


6

This question is very important and possible to answer empirically, however, words and concepts do not map 1:1 across languages so the mentioned assumption that bilingual dictionaries will have a great impact is speculative. Relative to what we might expect based on economic factors and inherent difficulty, machine translation quality lags for: English to ...


6

Certainly! Humans can do this too; computers are just more consistent at it. In general, language usage doesn't just come down to what's grammatical and what's not. A man or a woman can say "I like apples" and have it be equally correct. The differences come in when there are multiple ways to express the same concept. For example, Jespersen(1) conjectured ...


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