Machine translationMachine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.
TranslationTranslation is the communication of the meaning of a source-language text by means of an equivalent target-language text. The English language draws a terminological distinction (which does not exist in every language) between translating (a written text) and interpreting (oral or signed communication between users of different languages); under this distinction, translation can begin only after the appearance of writing within a language community.
Grammatical tenseIn grammar, tense is a that expresses time reference. Tenses are usually manifested by the use of specific forms of verbs, particularly in their conjugation patterns. The main tenses found in many languages include the past, present, and future. Some languages have only two distinct tenses, such as past and nonpast, or future and nonfuture. There are also tenseless languages, like most of the Chinese languages, though they can possess a future and nonfuture system typical of Sino-Tibetan languages.
Dictionary-based machine translationMachine translation can use a method based on dictionary entries, which means that the words will be translated as a dictionary does – word by word, usually without much correlation of meaning between them. Dictionary lookups may be done with or without morphological analysis or lemmatisation. While this approach to machine translation is probably the least sophisticated, dictionary-based machine translation is ideally suitable for the translation of long lists of phrases on the subsentential (i.e.
Automatic summarizationAutomatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document.
Parallel textA parallel text is a text placed alongside its translation or translations. Parallel text alignment is the identification of the corresponding sentences in both halves of the parallel text. The Loeb Classical Library and the Clay Sanskrit Library are two examples of dual-language series of texts. Reference Bibles may contain the original languages and a translation, or several translations by themselves, for ease of comparison and study; Origen's Hexapla (Greek for "sixfold") placed six versions of the Old Testament side by side.
PreteriteThe preterite or preterit (ˈprɛtərɪt; abbreviated or ) is a grammatical tense or verb form serving to denote events that took place or were completed in the past; in some languages, such as Spanish, French, and English, it is equivalent to the simple past tense. In general, it combines the perfective aspect (event viewed as a single whole; it is not to be confused with the similarly named perfect) with the past tense and may thus also be termed the perfective past.
Google TranslateGoogle Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. As of 2022, Google Translate supports languages at various levels; it claimed over 500 million total users , with more than 100 billion words translated daily, after the company stated in May 2013 that it served over 200 million people daily.
TreebankIn linguistics, a treebank is a parsed text corpus that annotates syntactic or semantic sentence structure. The construction of parsed corpora in the early 1990s revolutionized computational linguistics, which benefitted from large-scale empirical data. The term treebank was coined by linguist Geoffrey Leech in the 1980s, by analogy to other repositories such as a seedbank or bloodbank. This is because both syntactic and semantic structure are commonly represented compositionally as a tree structure.
Text corpusIn linguistics and natural language processing, a corpus (: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. In search technology, a corpus is the collection of documents which is being searched.
Speech translationSpeech translation is the process by which conversational spoken phrases are instantly translated and spoken aloud in a second language. This differs from phrase translation, which is where the system only translates a fixed and finite set of phrases that have been manually entered into the system. Speech translation technology enables speakers of different languages to communicate. It thus is of tremendous value for humankind in terms of science, cross-cultural exchange and global business.
Evaluation of machine translationVarious methods for the evaluation for machine translation have been employed. This article focuses on the evaluation of the output of machine translation, rather than on performance or usability evaluation. Round-trip translation A typical way for lay people to assess machine translation quality is to translate from a source language to a target language and back to the source language with the same engine. Though intuitively this may seem like a good method of evaluation, it has been shown that round-trip translation is a "poor predictor of quality".
Translation memoryA translation memory (TM) is a database that stores "segments", which can be sentences, paragraphs or sentence-like units (headings, titles or elements in a list) that have previously been translated, in order to aid human translators. The translation memory stores the source text and its corresponding translation in language pairs called “translation units”. Individual words are handled by terminology bases and are not within the domain of TM.
Uses of English verb formsThis article describes the uses of various verb forms in modern standard English language. This includes: Finite verb forms such as go, goes and went Nonfinite forms such as (to) go, going and gone Combinations of such forms with auxiliary verbs, such as was going and would have gone The uses considered include expression of tense (time reference), aspect, mood, modality and voice, in various configurations. For details of how inflected forms of verbs are produced in English, see English verbs.
Statistical machine translationStatistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory.
Word-sense disambiguationWord-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.
Simple pastThe simple past or past indefinite, sometimes called the preterite, is the basic form of the past tense in Modern English. It is used principally to describe events in the past, although it also has some other uses. Regular English verbs form the simple past in -ed; however, there are a few hundred irregular verbs with different forms. The term "simple" is used to distinguish the syntactical construction whose basic form uses the plain past tense alone, from other past tense constructions which use auxiliaries in combination with participles, such as the past perfect and past progressive.
ParticipleIn linguistics, a participle (; abbr. ) is a nonfinite verb form that has some of the characteristics and functions of both verbs and adjectives. More narrowly, participle has been defined as "a word derived from a verb and used as an adjective, as in a laughing face". "Participle" is a traditional grammatical term from Greek and Latin that is widely used for corresponding verb forms in European languages and analogous forms in Sanskrit and Arabic grammar.
French languageFrench (Standard French: Français fʁɑ̃sɛ or langue française lɑ̃ɡ fʁɑ̃sɛz) is a Romance language of the Indo-European family. It descended from the Vulgar Latin of the Roman Empire, as did all Romance languages. French evolved from Gallo-Romance, the Latin spoken in Gaul, and more specifically in Northern Gaul. Its closest relatives are the other langues d'oïl—languages historically spoken in northern France and in southern Belgium, which French (Francien) largely supplanted.
Tense–aspect–moodTense–aspect–mood (commonly abbreviated ) or tense–modality–aspect (abbreviated as ) is a group of that are important to understanding spoken or written content, and which are marked in different ways by different languages. TAM covers the expression of three major components of words which lead to or assist in the correct understanding of the speaker's meaning: Tense—the position of the state or action in time, that is, whether it is in the past, present or future.
