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New Algorithm for Learning Languages
An anonymous reader writes "U.S. and Israeli researchers have developed a method for enabling a computer program to scan text in any of a number of languages, including English and Chinese, and autonomously and without previous information infer the underlying rules of grammar. The rules can then be used to generate new and meaningful sentences. The method also works for such data as sheet music or protein sequences."
Paper here for those who have PNAS access.
http://en.wikipedia.org/wiki/Markov_chain
Used this (easy to compile) C program:
http://www.eblong.com/zarf/markov/
to create these:
http://www.mintruth.com/mirror/texts/
Mod points to whomever can tell us what texts they use. (No mod points can actually be given)
Get your Unix fortune now!
Unsupervised learning of natural languages
Zach Solan, David Horn, Eytan Ruppin and Shimon Edelman
School of Physics and Astronomy and School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel; and Department of Psychology, Cornell University, Ithaca, NY 14853
We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The ADIOS (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.
Many types of sequential symbolic data possess structure that is (i) hierarchical and (ii) context-sensitive. Natural-language text and transcribed speech are prime examples of such data: a corpus of language consists of sentences defined over a finite lexicon of symbols such as words. Linguists traditionally analyze the sentences into recursively structured phrasal constituents (1); at the same time, a distributional analysis of partially aligned sentential contexts (2) reveals in the lexicon clusters that are said to correspond to various syntactic categories (such as nouns or verbs). Such structure, however, is not limited to the natural languages; recurring motifs are found, on a level of description that is common to all life on earth, in the base sequences of DNA that constitute the genome. We introduce an unsupervised algorithm that discovers hierarchical structure in any sequence data, on the basis of the minimal assumption that the corpus at hand contains partially overlapping strings at multiple levels of organization. In the linguistic domain, our algorithm has been successfully tested both on artificial-grammar output and on natural-language corpora such as ATIS (3), CHILDES (4), and the Bible (5). In bioinformatics, the algorithm has been shown to extract from protein sequences syntactic structures that are highly correlated with the functional properties of these proteins.
The ADIOS Algorithm for Grammar-Like Rule Induction
In a machine learning paradigm for grammar induction, a teacher produces a sequence of strings generated by a grammar G0, and a learner uses the resulting corpus to construct a grammar G, aiming to approximate G0 in some sense (6). Recent evidence suggests that natural language acquisition involves both statistical computation (e.g., in speech segmentation) and rule-like algebraic processes (e.g., in structured generalization) (7-11). Modern computational approaches to grammar induction integrate statistical and rule-based methods (12, 13). Statistical information that can be learned along with the rules may be Markov (14) or variable-order Markov (15) structure for finite state (16) grammars, in which case the EM algorithm can be used to maximize the likelihood of the observed data. Likewise, stochastic annotation for context-free grammars (CFGs) can be learned by using methods such as the Inside-Outside algorithm (14, 17).
We have developed a method that, like some of those just mentioned, combines statistics and rules: our algorithm, ADIOS (for automatic distillation of structure) uses statistical information present in raw sequential data to identify significant segments and to distill rule-like regularities that support structured generalization. Unlike
Try this free website out. http://www.freetranslation.com/
I know it is fairly accurate because I have fooled my spanish speaking friends once in an IM conversation. I told them I learned spanish via hypnosis and basically just copy/pasted everything spanish into IM. The conversation went on for like 15 minutes full spanish before I told them I was using the website. They were pissing their pants.
What they've develloped is something which interprets grammar; the ruleset behind the organisation of buildingblocks, apparently buildingblock agnostic.
A dictionary is just words. This algorythm cant assign meaning to the buildingblocks, it can only dicide how and in what order the buildingblocks go together.
-- Waht? Tehr's a preveiw buottn?
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Time flies like an arrow.
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Fruit flies like a banana.
There are other, similar examples. Computer systems tend to deduce either that there's a type of insect called "time flies", or that the latter sentence refers to the aerodynamic properties of fruit.Yes! I'd have thrown a mod point at you just for this paragraph if I could.
English is very precise (when used as directed) in matters of time and sequence -- we have more than 20 verb tenses where most languages get away with three.
Not really. Firstly, English only has two or three tenses. (Depending upon which linguist you ask, English either has a past/non-past distinction or past/present/future distinctions. See [1], [2]. The general consensus seems to be in favor of the former, although I humbly disagree with the general consensus.) It maintains a variety of aspect distinctions (perfective vs imperfective, habitual vs continuous, nonprogressive vs progressive). See [3]. Its verbs also interact with modality, albeit slightly less strongly.
It's a very common mistake to count the combinations of tense, aspect, and modality in a language and arrive at some astronomical number of "tenses". It's an even more common mistake (for native English speakers, anyway) to think that English is special or different or strange compared to other languages. In most cases, it's not -- especially when compared with other Indo-European languages.
Secondly, and more interestingly IMHO, most languages do not have three distinct tenses. The most common cases are either to have a future/non-future distinction or a past/non-past distinction. In any case, the future tense, if it exists, is normally derived from modal or aspectual markers and is diachronically weak (which is linguist-babble meaning "future tenses forms don't stick around for very long"). See [3].
English is a perfect example: will, of course, used to refer to the agent's desire (his or her will) to do something. Only recently has it shifted to have a more temporal sense, and it still maintains some of its modal flavor. In fact, the least marked way of making the future (in the US, at least) is to use either gonna or a present progressive form: I'm having dinner with my boss tonight. I'm gonna ask him for a raise. See Comrie [1] again.
So as not to be anglo-centric, I'll give another example. Spanish has three widespread means of forming the future tense. Two of these are periphrastic and are exemplified by he de cantar 'I've gotta sing' and voy a cantar 'I'm gonna sing'. The last is the synthetic form, cantaré 'I'll sing'.
Most high school or college Spanish teachers would tell you that the "pure" future is cantaré. Actually, it's historically derived from the phrase cantar he 'I have to sing' (from Latin cantáre habeo), and is being displaced by the other two forms all across the Spanish-speaking world. I'm told, for example, that cantaré has been largely lost in in Argentina and southern Chile (see [4]).
In any case, the parent's main point still holds. It's a b?tch to deal with cross-linguistic differences in major semantic systems computationally. But good lord, it's fun to try. :)
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