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N-gram
An ''n''-gram is a sequence of ''n'' adjacent symbols in particular order. The symbols may be ''n'' adjacent letter (alphabet), letters (including punctuation marks and blanks), syllables, or rarely whole words found in a language dataset; or adjacent phonemes extracted from a speech-recording dataset, or adjacent base pairs extracted from a genome. They are collected from a text corpus or speech corpus. If Latin numerical prefixes are used, then ''n''-gram of size 1 is called a "unigram", size 2 a "bigram" (or, less commonly, a "digram") etc. If, instead of the Latin ones, the Cardinal number (linguistics), English cardinal numbers are furtherly used, then they are called "four-gram", "five-gram", etc. Similarly, using Greek numerical prefixes such as "monomer", "dimer", "trimer", "tetramer", "pentamer", etc., or English cardinal numbers, "one-mer", "two-mer", "three-mer", etc. are used in computational biology, for polymers or oligomers of a known size, called k-mer, ''k'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word N-gram Language Model
A word ''n''-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words. If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if ''n'' − 1 words, an ''n''-gram model. Special tokens are introduced to denote the start and end of a sentence \langle s\rangle and \langle /s\rangle. To prevent a zero probability being assigned to unseen words, each word's probability is slightly higher than its frequency count in a corpus. To calculate it, various methods were used, from simple "add-one" smoothing (assign a count of 1 to unseen ''n''-grams, as an uninformative prior) to more sophisticated models, such as Good–Turing discounting or Katz's back-off model, back-off models. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis. Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker-independent" systems. Systems that use training are called "speaker dependent". Speech recognition applications include voice user interfaces ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Modeling
A language model is a model of the human brain's ability to produce natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation,Andreas, Jacob, Andreas Vlachos, and Stephen Clark (2013)"Semantic parsing as machine translation". Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word ''n''-gram language model. History Noam Chomsky did pioneering work on lang ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Model
In probability theory, a Markov model is a stochastic model used to Mathematical model, model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be Intractability (complexity), intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property. Introduction Andrey Andreyevich Markov (14 June 1856 – 20 July 1922) was a Russian mathematician best known for his work on stochastic processes. A primary subject of his research later became known as the Markov chain. There are four common Markov models used in different situations, depending on whether every sequential state is observable or not, and whether the system is to be adjusted on the basis of observation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Linguistics
Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others. Computational linguistics is closely related to mathematical linguistics. Origins The field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Processing
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Major tasks in natural language processing are speech recognition, text classification, natural-language understanding, natural language understanding, and natural language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. The proposed test includes a task that involves the automated interpretation and generation of natural language ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Corpus Of Contemporary American English
The Corpus of Contemporary American English (COCA) is a one-billion-word corpus of contemporary American English. It was created by Mark Davies, retired professor of corpus linguistics at Brigham Young University (BYU). Content The Corpus of Contemporary American English (COCA) is composed of one billion words as of November 2021. The corpus is constantly growing: In 2009 it contained more than 385 million words; in 2010 the corpus grew in size to 400 million words; by March 2019, the corpus had grown to 560 million words. As of November 2021, the Corpus of Contemporary American English is composed of 485,202 texts. According to the corpus website, the current corpus (November 2021) is composed of texts that include 24-25 million words for each year 1990–2019. For each year contained in the corpus (1990–2019), the corpus is evenly divided between six registers/genres: TV/movies, spoken, fiction, magazine, newspaper, and academic (see Texts and Registers page of the COCA w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google Books Ngram Viewer
The Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of ''n''-grams found in printed sources published between 1500 and 2022 in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish. There are also some specialized English corpora, such as American English, British English, and English Fiction. The program can search for a word or a phrase, including misspellings or gibberish. The ''n''-grams are matched with the text within the selected corpus, and if found in 40 or more books, are then displayed as graph The Google Books Ngram Viewer supports searches for parts of speech and wildcards. It is routinely used in research. History In the development processes, Google teamed up with two Harvard researchers, Jean-Baptiste Michel and Erez Lieberman Aiden, and quietly released the program on December 16, 2010. Before the release, it was difficult ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Character (computing)
In computing and telecommunications, a character is the internal representation of a character (symbol) used within a computer or system. Examples of characters include letters, numerical digits, punctuation marks (such as "." or "-"), and whitespace. The concept also includes control characters, which do not correspond to visible symbols but rather to instructions to format or process the text. Examples of control characters include carriage return and tab as well as other instructions to printers or other devices that display or otherwise process text. Characters are typically combined into '' strings''. Historically, the term ''character'' was used to denote a specific number of contiguous bits. While a character is most commonly assumed to refer to 8 bits (one byte) today, other options like the 6-bit character code were once popular, and the 5-bit Baudot code has been used in the past as well. The term has even been applied to 4 bits with only 16 possible valu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Base Pair
A base pair (bp) is a fundamental unit of double-stranded nucleic acids consisting of two nucleobases bound to each other by hydrogen bonds. They form the building blocks of the DNA double helix and contribute to the folded structure of both DNA and RNA. Dictated by specific hydrogen bonding patterns, "Watson–Crick" (or "Watson–Crick–Franklin") base pairs (guanine–cytosine and adenine–thymine) allow the DNA helix to maintain a regular helical structure that is subtly dependent on its nucleotide sequence. The Complementarity (molecular biology), complementary nature of this based-paired structure provides a Redundancy (information theory), redundant copy of the genetic information encoded within each strand of DNA. The regular structure and data redundancy provided by the DNA double helix make DNA well suited to the storage of genetic information, while base-pairing between DNA and incoming nucleotides provides the mechanism through which DNA polymerase replicates DNA and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
