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.
Speech recognitionSpeech 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.
Lexical resourceIn digital lexicography, natural language processing, and digital humanities, a lexical resource is a language resource consisting of data regarding the lexemes of the lexicon of one or more languages e.g., in the form of a database. Different standards for the machine-readable edition of lexical resources exist, e.g., Lexical Markup Framework (LMF) an ISO standard for encoding lexical resources, comprising an abstract data model and an XML serialization, and OntoLex-Lemon, an RDF vocabulary for publishing lexical resources as knowledge graphs on the web, e.
Lexical Markup FrameworkLanguage resource management Lexical markup framework (LMF; ISO 24613:2008), is the International Organization for Standardization ISO/TC37 standard for natural language processing (NLP) and machine-readable dictionary (MRD) lexicons. The scope is standardization of principles and methods relating to language resources in the contexts of multilingual communication. The goals of LMF are to provide a common model for the creation and use of lexical resources, to manage the exchange of data between and among these resources, and to enable the merging of large number of individual electronic resources to form extensive global electronic resources.
Acoustic modelAn acoustic model is used in automatic speech recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts. It is created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word. Modern speech recognition systems use both an acoustic model and a language model to represent the statistical properties of speech.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
DictionaryA dictionary is a listing of lexemes from the lexicon of one or more specific languages, often arranged alphabetically (or by consonantal root for Semitic languages or radical and stroke for logographic languages), which may include information on definitions, usage, etymologies, pronunciations, translation, etc. It is a lexicographical reference that shows inter-relationships among the data. A broad distinction is made between general and specialized dictionaries.
Language modelA language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.
PronunciationPronunciation is the way in which a word or a language is spoken. This may refer to generally agreed-upon sequences of sounds used in speaking a given word or language in a specific dialect ("correct" or "standard" pronunciation) or simply the way a particular individual speaks a word or language. Words' pronunciations can be found in reference works such as dictionaries. General-purpose dictionaries typically only include standard pronunciations, but regional or dialectal pronunciations may be found in more specific works.
Greek languageGreek (Elliniká, eliniˈka; Hellēnikḗ) is an independent branch of the Indo-European family of languages, native to Greece, Cyprus, Italy (in Calabria and Salento), southern Albania, and other regions of the Balkans, the Black Sea coast, Asia Minor, and the Eastern Mediterranean. It has the longest documented history of any Indo-European language, spanning at least 3,400 years of written records. Its writing system is the Greek alphabet, which has been used for approximately 2,800 years; previously, Greek was recorded in writing systems such as Linear B and the Cypriot syllabary.
BabelNetBabelNet is a multilingual lexicalized semantic network and ontology developed at the NLP group of the Sapienza University of Rome. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Transcription (linguistics)Transcription in the linguistic sense is the systematic representation of spoken language in written form. The source can either be utterances (speech or sign language) or preexisting text in another writing system. Transcription should not be confused with translation, which means representing the meaning of text from a source-language in a target language, (e.g. Los Angeles (from source-language Spanish) means The Angels in the target language English); or with transliteration, which means representing the spelling of a text from one script to another.
Speaker recognitionSpeaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to speaker recognition or speech recognition. Speaker verification (also called speaker authentication) contrasts with identification, and speaker recognition differs from speaker diarisation (recognizing when the same speaker is speaking).
Modern GreekModern Greek (Νέα Ελληνικά, Néa Elliniká, ˈne.a eliniˈka or Κοινή Νεοελληνική Γλώσσα, Kiní Neoellinikí Glóssa), generally referred to by speakers simply as Greek (Ελληνικά, Elliniká), refers collectively to the dialects of the Greek language spoken in the modern era, including the official standardized form of the languages sometimes referred to as Standard Modern Greek.
Japanese dictionaryhave a history that began over 1300 years ago when Japanese Buddhist priests, who wanted to understand Chinese sutras, adapted Chinese character dictionaries. Present-day Japanese lexicographers are exploring computerized editing and electronic dictionaries. According to Nakao Keisuke (中尾啓介): It has often been said that dictionary publishing in Japan is active and prosperous, that Japanese people are well provided for with reference tools, and that lexicography here, in practice as well as in research, has produced a number of valuable reference books together with voluminous academic studies.
Ancient GreekAncient Greek includes the forms of the Greek language used in ancient Greece and the ancient world from around 1500 BC to 300 BC. It is often roughly divided into the following periods: Mycenaean Greek (1400–1200 BC), Dark Ages (1200–800 BC), the Archaic or Epic period (800–500 BC), and the Classical period (500–300 BC). Ancient Greek was the language of Homer and of fifth-century Athenian historians, playwrights, and philosophers.
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.
Optical character recognitionOptical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).
Sentence processingSentence processing takes place whenever a reader or listener processes a language utterance, either in isolation or in the context of a conversation or a text. Many studies of the human language comprehension process have focused on reading of single utterances (sentences) without context. Extensive research has shown that language comprehension is affected by context preceding a given utterance as well as many other factors. Sentence comprehension has to deal with ambiguity in spoken and written utterances, for example lexical, structural, and semantic ambiguities.
