This classification is the best guess by the Glottolog editors and the classification principles are described in Figure 1 below and the accompanying text. Users should be aware that for many groups of languages, there is little available historical-comparative research, so the classifications are subject to change as scholarship and interest in those languages increase. Please contact the editors if you have corrections to the language classification. In addition to the genealogical trees families and isolates , the Families page also includes the following non-genealogical trees :.
Glottolog also contains lists of putative languages that are not regarded as real languoids by the editors but that are given a Glottocode for bookkeeping purposes; these are called bookkeeping languoids and they are described further below. Every putative language is considered according to the decision procedure in Figure 1. All spoken languages for which a sufficient amount of linguistic data exists—the leaves of the decision tree with double boxes around them—are deemed classifiable, and are classified into genealogical families and isolates. The other kinds of languages are filed into the other categories that were listed above.
Glottolog is complete only for classifiable languages. A comprehensive listing of pidgins is Peter Bakker and Mikael Parkvall Thierry , initiation languages Ngonga-ke-Mbembe, E. Aitken For any alleged language to be considered in the classification we must first determine whether it was distinct from all other languages. By distinct, we mean not mutually intelligible with any other language.
In principle, any convincing evidence to this effect is sufficient. For example, direct comparison of language data or testimonies of non-intelligibility to all neighbouring languages is the most straightforward kind of evidence. But also, various types of evidence for isolation from all other humans for a long time could make a convincing case that a language is indeed distinct from all others. Ethnographic evidence suggests that they, if akin to anyone in the vicinity, are Kanamari a known Katukinan language, see, e.
However, Scott Wallace recounts one meeting between a Kanamari and the Flecheiros revealing that they do not speak intelligible languages though one Kanamari woman captured at an early age was living among the Flecheiros. Even if not totally foolproof, this appears to be convincing evidence that the Flecheiros speak a language distinct from all others.
However, all the pieces of evidence must be present. There are plenty of other cases where a speech form often extinct is known not to have been unintelligible to some or most languages around it e. Eberhard , but this is not sufficient if it cannot be asserted for every plausible candidate. A further caveat is that testimonies must themselves be convincing to count as testimonies.
There are cases where unintelligibility information comes from individuals who were in no position to judge it, e. In the latter case, it is listed as a type of bookkeping languoid see below. For a linguistic classification, we naturally require that actual linguistic data, i. That means that some linguistic data has been collected which provides the basis for classification, but does not necessarily mean that the data in question has been published.
We also require that the data is not known to have vanished, meaning that once attested languages whose attestation now appears to be lost count as unattested. Brinton , now seem to have vanished completely. Thus, the three count as unattested because it is known that the attestation is gone. There are two reasons for restricting the scope to communication systems that serve d as the main means of communication for a human society. First, language classification see below by the comparative method explicitly or implicitly assumes that language change is governed by certain vaguely formulated probabilistic laws.
These laws have a plausible theoretical foundation if the communication system serve d as the main means of communication for a human society, but do not necessarily apply to all forms of normed human communication systems. For example, radical vocabulary replacement within one generation of speakers would be highly unlikely for a main means of communication of a society communication would break down!
Similarly, sound change is though to come about as humans hear and mis interpret spoken analog communication John J. Ohala , Brown, Cecil H. Second, one of the purposes for doing language classification in the first place is to obtain insights into the history of its speakers.
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All human societies have a main means of communication, so such a communication system reflects the history of a human society. It is not necessarily the case that all forms of normed human communication systems reflect the history of its speakers. For example, a whistled language may come and go in the course of history of a people, whereas a people cannot be without a main speech form for any period of history. The network in the bottom receives the sequence in the original order, while the network in the top takes receives the same input but in reverse order.https://ignamant.cl/wp-includes/64/2245-gps-para-espiar.php
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Both networks are not necessarily identical. Important is, their outputs are combined for the final prediction. We had enough of theory. Now, we will implement a LSTM network for predicting the probability of the next character in a sequence, based on the characters already observed in the sequence.
Our sequences will be words from a not so popular language, called Yemba. You might have never heard about that language before. Feel at ease to look at pictograms below to get an idea of Yemba writing. Yemba is an African language spoken by just few thousand native speakers today. Despite originally being a spoken language exclusively, Yemba writing was developed ca. Like so many languages in the world, Yemba is a tone language, similar to Vietnamese.
In tone languages, words are made of consonants, vowels; and tones — the variation of musical pitch which accompanies the utterance of a syllable. The foundational model of tone orthography in Yemba was put in place by His Majesty Chief Djoumessi Mathias of Foreke-Dschang , the pioneer who designed the first Yemba alphabet in Later, in a modern Yemba-French dictionary was created as a result of a joined international research effort. Our goal is to encode Yemba words as embeddings vectors, and to build a LSTM network that is able to predict whether a Yemba word is either a noun or a verb, by looking only at the characters and tones present in the word.
We do not aim to implement a part-of-speech tagging. Rather, we will train the network to learn groups of letters and tones which commonly appear in Yemba nouns, compared to those which are specific to Yemba verbs. For this purpose, we use a pre-processed English-Yemba dataset downloaded from the Yemba. Above we can see few words from the dictionary. You can try to read them if you want to have good mood. Actually, Yemba writing is based on the International Phonetic Language.
Anyone with knowledge of phonetics could actually be able to read and speak Yemba! Although we restricted our dataset to nouns and verbs, Yemba also includes adjectives, adverbs, conjunctions, pronouns, etc. The distribution of word types is shown below. Below we show few statistics about our dataset. Our Yemba words are built from a 45 letters alphabet vocabulary.
The vocabulary represents each Yemba letter by a unique integer. This is a typical preprocessing step in natural language processing. Before feeding the words into a LSTM, we have to tokenize each word, by replacing each letter in the word with its index from the vocabulary. This process turns the word into a vector of numbers X. In order to have the same vector size for all words, we pad the vectors to the length of the longest word in our dataset. Our LSTM will learn to match those vectors to the correct word type: 0 for a noun and 1 for a verb.
Therefore we also build a vector of labels Y to store the correct classes. Next, we split our vectors into a training set with words, and a validation set with words. Below we build a 1-layer LSTM with cells.
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We do not feed the words vector directly to the LSTM. Instead we first learn their embeddings representation in a 8-dimensional space. Embeddings are known to capture the semantic relationships between the letters building the word. The output from the LSTM is transformed by a fully connected layer with sigmoid activation to produce a probability between 0 and 1. We train the network by using binary crossentropy as loss function and Adam as optimizer. Classification accuracy is used as metric for evaluating the classification performance since our two classes are pretty well balanced.
As shown above, the network achieves convergence very fast. The LSTM is doing a great job, by predicting with Training was a little bit faster, and accuracy was a little bit better, The confusion matrix exhibits few false positives and false negatives: 1 verb was predicted as noun and 2 nouns were predicted to be verbs out of The miss-classified words are shown below. It appears that this semantic or grammatical construct was correctly picked up by our character-based LSTM. Therefore, the LSTM predicted it as verb, although it is a noun. The twelfth digit is a check digit and usually appears at the bottom right of the symbol.
Enter all digits found on the item e. An ISSN is a standardized international code which allows the identification of a serial publication. An ISSN consists of eight digits in two groups of four, separated by a hyphen.
Nilo-Saharan languages | syzezocyvida.tk
You can enter an ISSN with or without a hyphen or leading zeros as shown below: FAST headings provide additional subject information about a work and enable you to search the Classify database by subject. OCLC is a non-profit library cooperative, made up of thousands of member libraries throughout the world. OCLC does not sell books and the actual content is not provided in our database. Libraries add bibliographic records to WorldCat representing books and other items in their collections. The Classify prototype helps librarians apply classification numbers to resources in library collections.
Your local library may be able to help you gain access to a resource found through Classify and WorldCat. Classification schemes are used to provide a systematic arrangement of materials.
Related Language Classification by Numbers
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