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BulSemCor : All words in BulSemCor are assigned a sense, while according to established practice only simple content words or content word classes (typically nouns and verbs) are annotated. Since 2000 the development of language resources, has broadened to include annotation of function words and multiword expressions ... |
BulSemCor : Corpus linguistics Natural language processing Bulgarian National Corpus Bulgarian WordNet BulPosCor |
BulSemCor : Koeva, Svetla (2010). "Balgarskiyat semantichno anotiran korpus" [The Bulgarian Sense-annotated Corpus]. Koeva, Svetla; Leseva, S.; Todorova, M. (May 23, 2006). Bulgarian Sense Tagged Corpus. 5th SALTMIL Workshop on Minority Languages: Strategies for Developing Machine Translation for Minority Languages. pp... |
BulSemCor : BulSemCor Search Interface BulSemCor in META-SHARE BulNet in META-SHARE Department of Computational Linguistics |
Time aware long short-term memory : Time Aware LSTM (T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowl... |
Biomedical data science : Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding bio... |
Biomedical data science : The National Library of Medicine of the US National Institutes of Health (NIH) identified key biomedical data scientist attributes in an NIH-wide review: general biomedical subject matter knowledge; programming language expertise; predictive analytics, modeling, and machine learning; team scie... |
Biomedical data science : The Human Genome Project (HGP), which uncovered the DNA sequences that compose human genes, would not have been possible without biomedical data science. Significant computational resources were required to process the data in the HGP, as the human genome contains over 6 billion DNA base pairs... |
WaveNet : WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind. The technique, outlined in a paper in September 2016, is able to generate relatively realistic-sounding human-like voices by directly modelling waveforms using a neural network method tra... |
WaveNet : Generating speech from text is an increasingly common task thanks to the popularity of software such as Apple's Siri, Microsoft's Cortana, Amazon Alexa and the Google Assistant. Most such systems use a variation of a technique that involves concatenated sound fragments together to form recognisable sounds and... |
WaveNet : At the time of its release, DeepMind said that WaveNet required too much computational processing power to be used in real world applications. As of October 2017, Google announced a 1,000-fold performance improvement along with better voice quality. WaveNet was then used to generate Google Assistant voices fo... |
WaveNet : 15.ai Deep learning speech synthesis |
WaveNet : WaveNet: A Generative Model for Raw Audio |
Lexxe : Lexxe is an internet search engine that applies Natural Language Processing in its semantic search technology. Founded in 2005 by Dr. Hong Liang Qiao, Lexxe is based in Sydney, Australia. Today, Lexxe's key focus is on sentiment search with the launch of a news sentiment search site at News & Moods (www.newsand... |
Lexxe : Lexxe main web site News & Moods web site |
GPTZero : GPTZero is an artificial intelligence detection software developed to identify artificially generated text, such as those produced by large language models. While GPTZero was praised for its efforts to prevent academic dishonesty, many news outlets criticized the tool's false positive rate, which can be espec... |
GPTZero : GPTZero was developed by Edward Tian, a Princeton University undergraduate student, and launched online in January 2023 in response to concerns about AI-generated usage in academic plagiarism. GPTZero said in May 2023 it raised over 3.5 million dollars in seed funding. In the first week of its release, the GP... |
GPTZero : GPTZero uses qualities it terms perplexity and burstiness to attempt determining if a passage was written by a AI. According to the company, perplexity is how random the text in the sentence is, and whether the way the sentence is constructed is unusual or "surprising" for the application. Texts with language... |
GPTZero : The academic community has attempted using GPTZero to tackle concerns about AI-generated content for plagiarism. Educational institutions, including Princeton University, have discussed the use of GPTZero to combat AI-generated content in academic settings, with mixed opinions. In October 2023, GPTZero had pa... |
GPTZero : In a March 2023 paper named "Can AI-Generated Text be Reliably Detected?", computer scientists Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi from the University of Maryland demonstrate empirically and theoretically that several AI-text detectors are not reliable i... |
GPTZero : Artificial intelligence content detection Ethics of artificial intelligence Natural language processing ChatGPT Text generation Turing test |
GPTZero : Cassidy, Caitlin (January 11, 2023). "College student claims app can detect essays written by chatbot ChatGPT". The Guardian. |
Tatoeba : Tatoeba is a free collection of example sentences with translations geared towards foreign language learners. It is available in more than 400 languages. Its name comes from the Japanese phrase tatoeba (例えば), meaning 'for example'. It is written and maintained by a community of volunteers through a model of o... |
Tatoeba : In 2006, Trang Ho was frustrated that unlike some of their Japanese counterparts, German bilingual dictionaries didn't feature full-text search of usage examples with translations. It led her to imagine her ideal dictionary and to build a prototype hosted on SourceForge under the name "multilangdict." The mai... |
Tatoeba : As founder of Tatoeba, Trang Ho has long been the project's BDFL. In 2011, she set up a nonprofit organization to oversee the project. In 2022, she decided to step aside in favor of a small group of experienced Tatoebans. |
Tatoeba : As of February 2025, the Tatoeba Corpus has over 12,600,000 sentences in 426 languages. 66 of these languages have 10,000 or more sentences. Over 1 million sentences have audio recordings. The sentences are interrelated within a graph that has more than 25,900,000 links. 276 language pairs have over 10,000 tr... |
Tatoeba : Tatoeba received a grant from Mozilla Drumbeat in December 2010. Some work on the Tatoeba infrastructure was sponsored by Google Summer of Code, 2014 edition. Since 2014, Tatoeba has been supported by donations. In May 2018 they received a $25,000 Mozilla Open Source Support (MOSS) program grant. In August 20... |
Tatoeba : Phrase book Parallel text Common Voice Lingua Libre Wiktionary |
Tatoeba : Official website Video of Trang Ho introducing Tatoeba at MozFest 2019 Tatoeba's statistics Tatoeba Translation Challenge |
Generative topographic map : Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first... |
Generative topographic map : The approach is strongly related to density networks which use importance sampling and a multi-layer perceptron to form a non-linear latent variable model. In the GTM the latent space is a discrete grid of points which is assumed to be non-linearly projected into data space. A Gaussian nois... |
Generative topographic map : In data analysis, GTMs are like a nonlinear version of principal components analysis, which allows high-dimensional data to be modelled as resulting from Gaussian noise added to sources in lower-dimensional latent space. For example, to locate stocks in plottable 2D space based on their hi-... |
Generative topographic map : While nodes in the self-organizing map (SOM) can wander around at will, GTM nodes are constrained by the allowable transformations and their probabilities. If the deformations are well-behaved the topology of the latent space is preserved. The SOM was created as a biological model of neuron... |
Generative topographic map : Self-organizing map (SOM) Neural network (machine learning) aka Artificial Neural Network (ANN) Connectionism Data mining Machine learning Nonlinear dimensionality reduction Neural network software Pattern recognition |
Generative topographic map : Bishop, Svensen and Williams Generative Topographic Mapping paper Generative topographic mapping developed at the Neural Computing Research Group os Aston University (UK). ( Matlab toolbox ) |
Helmholtz machine : The Helmholtz machine (named after Hermann von Helmholtz and his concept of Helmholtz free energy) is a type of artificial neural network that can account for the hidden structure of a set of data by being trained to create a generative model of the original set of data. The hope is that by learning... |
Helmholtz machine : Autoencoder Boltzmann machine Hopfield network Restricted Boltzmann machine |
Helmholtz machine : http://www.cs.utoronto.ca/~hinton/helmholtz.html — Hinton's papers on Helmholtz machines https://www.nku.edu/~kirby/docs/HelmholtzTutorialKoeln.pdf - A tutorial on Helmholtz machines |
Anaphora (linguistics) : In linguistics, anaphora () is the use of an expression whose interpretation depends upon another expression in context (its antecedent). In a narrower sense, anaphora is the use of an expression that depends specifically upon an antecedent expression and thus is contrasted with cataphora, whic... |
Anaphora (linguistics) : The term anaphora is actually used in two ways. In a broad sense, it denotes the act of referring. Any time a given expression (e.g. a pro-form) refers to another contextual entity, anaphora is present. In a second, narrower sense, the term anaphora denotes the act of referring backwards in a d... |
Anaphora (linguistics) : The term anaphor is used in a special way in the generative grammar tradition. Here it denotes what would normally be called a reflexive or reciprocal pronoun, such as himself or each other in English, and analogous forms in other languages. The use of the term anaphor in this narrow sense is u... |
Anaphora (linguistics) : In some cases, anaphora may refer not to its usual antecedent, but to its complement set. In the following example a, the anaphoric pronoun they refers to the children who are eating the ice-cream. Contrastingly, example b has they seeming to refer to the children who are not eating ice-cream: ... |
Anaphora (linguistics) : There are many theories that attempt to prove how anaphors are related and trace back to their antecedents, with centering theory (Grosz, Joshi, and Weinstein 1983) being one of them. Taking the computational theory of mind view of language, centering theory gives a computational analysis of un... |
Anaphora (linguistics) : King, Jeffrey C. "Anaphora". In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy. What is anaphora? |
Automatic hyperlinking : An autolink is a hyperlink added automatically to a hypermedia document, after it has been authored or published. Automatic hyperlinking describes the process or the software feature that produces autolinks. Segments of the hypermedia are identified through a process of pattern matching. For ex... |
Automatic hyperlinking : entity linking named entity recognition tf-idf autocomplete code folding == References == |
Collocation extraction : Collocation extraction is the task of using a computer to extract collocations automatically from a corpus. The traditional method of performing collocation extraction is to find a formula based on the statistical quantities of those words to calculate a score associated to every word pairs. Pr... |
Collocation extraction : Collocational restriction Collostructional analysis Compound noun, adjective and verb Phrasal verb Siamese twins (English language) Terminology extraction n-gram analysis |
Collocation extraction : What is collocation == References == |
Lemmatization : Lemmatization (or less commonly lemmatisation) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. In computational linguistics, lemmatization is the algorithmic process of determin... |
Lemmatization : In many languages, words appear in several inflected forms. For example, in English, the verb 'to walk' may appear as 'walk', 'walked', 'walks' or 'walking'. The base form, 'walk', that one might look up in a dictionary, is called the lemma for the word. The association of the base form with a part of s... |
Lemmatization : A trivial way to do lemmatization is by simple dictionary lookup. This works well for straightforward inflected forms, but a rule-based system will be needed for other cases, such as in languages with long compound words. Such rules can be either hand-crafted or learned automatically from an annotated c... |
Lemmatization : Morphological analysis of published biomedical literature can yield useful results. Morphological processing of biomedical text can be more effective by a specialized lemmatization program for biomedicine, and may improve the accuracy of practical information extraction tasks. |
Lemmatization : Canonicalization – Process for converting data into a "standard", "normal", or canonical form |
Lemmatization : == External links == |
Linguistic empathy : Linguistic empathy in theoretical linguistics is the "point of view" in an anaphoric utterance by which a participant is bound with or in the event or state that they describe in that sentence. An example is found with the Japanese verbs yaru and kureru. These both share the same essential meaning ... |
Linguistic empathy : The basic idea of linguistic empathy is that sentences can provide information about the speaker's point of view, from which they describe a state of affairs. This information can be expressed as concerning the speaker's identification with a participant", "camera angle", and "point of view". For e... |
Machine translation : Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural m... |
Machine translation : Before the advent of deep learning methods, statistical methods required a lot of rules accompanied by morphological, syntactic, and semantic annotations. |
Machine translation : Studies using human evaluation (e.g. by professional literary translators or human readers) have systematically identified various issues with the latest advanced MT outputs. Common issues include the translation of ambiguous parts whose correct translation requires common sense-like semantic lang... |
Machine translation : While no system provides the ideal of fully automatic high-quality machine translation of unrestricted text, many fully automated systems produce reasonable output. The quality of machine translation is substantially improved if the domain is restricted and controlled. This enables using machine t... |
Machine translation : There are many factors that affect how machine translation systems are evaluated. These factors include the intended use of the translation, the nature of the machine translation software, and the nature of the translation process. Different programs may work well for different purposes. For examp... |
Machine translation : In the early 2000s, options for machine translation between spoken and signed languages were severely limited. It was a common belief that deaf individuals could use traditional translators. However, stress, intonation, pitch, and timing are conveyed much differently in spoken languages compared t... |
Machine translation : Only works that are original are subject to copyright protection, so some scholars claim that machine translation results are not entitled to copyright protection because MT does not involve creativity. The copyright at issue is for a derivative work; the author of the original work in the origina... |
Machine translation : Cohen, J. M. (1986), "Translation", Encyclopedia Americana, vol. 27, pp. 12–15 Hutchins, W. John; Somers, Harold L. (1992). An Introduction to Machine Translation. London: Academic Press. ISBN 0-12-362830-X. Lewis-Kraus, Gideon (7 June 2015). "Tower of Babble". New York Times Magazine. pp. 48–52. ... |
Machine translation : The Advantages and Disadvantages of Machine Translation International Association for Machine Translation (IAMT) Archived 24 June 2010 at the Wayback Machine Machine Translation Archive Archived 1 April 2019 at the Wayback Machine by John Hutchins. An electronic repository (and bibliography) of ar... |
Mobile translation : Mobile translation is any electronic device or software application that provides audio translation. The concept includes any handheld electronic device that is specifically designed for audio translation. It also includes any machine translation service or software application for hand-held device... |
Mobile translation : A translation system allowing the Japanese to exchange conversations with foreign nationals through mobile phones was first developed in 1999 by the Advanced Telecommunications Research Institute International-Interpreting Telecommunications Research Laboratories, based in Kansai Science City, Japa... |
Mobile translation : In order to support the machine translation service, a mobile device needs to be able to communicate with external computers (servers) that receive the user-input text/speech, translate it and send it back to the user. This is usually done via an Internet connection (WAP, GPRS, EDGE, UMTS, Wi-Fi) b... |
Mobile translation : Overview of current technology Pixel Buds translates voice, but it's not the first:The headphones announced along with Google's Pixel 2 phone promise nearly-seamless futuristic voice translation. We've been promised this before. By Ian Sherr, October 4, 2017 |
Name resolution (semantics and text extraction) : In semantics and text extraction, name resolution refers to the ability of text mining software to determine which actual person, actor, or object a particular use of a name refers to. It can also be referred to as entity resolution. |
Name resolution (semantics and text extraction) : For example, in the text mining field, software frequently needs to interpret the following text: John gave Edward the book. He then stood up and called to John to come back into the room. In these sentences, the software must determine whether the pronoun "he" refers t... |
Name resolution (semantics and text extraction) : Frequently, this type of name resolution is also used across documents, for example to determine whether the "George Bush" referenced in an old newspaper article as President of the United States (George H. W. Bush) is the same person as the "George Bush" mentioned in a... |
Name resolution (semantics and text extraction) : Identity resolution Named entity recognition Naming collision Anaphor resolution == References == |
Named-entity recognition : Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organiz... |
Named-entity recognition : Notable NER platforms include: GATE supports NER across many languages and domains out of the box, usable via a graphical interface and a Java API. OpenNLP includes rule-based and statistical named-entity recognition. SpaCy features fast statistical NER as well as an open-source named-entity ... |
Named-entity recognition : In the expression named entity, the word named restricts the task to those entities for which one or many strings, such as words or phrases, stand (fairly) consistently for some referent. This is closely related to rigid designators, as defined by Kripke, although in practice NER deals with m... |
Named-entity recognition : NER systems have been created that use linguistic grammar-based techniques as well as statistical models such as machine learning. Hand-crafted grammar-based systems typically obtain better precision, but at the cost of lower recall and months of work by experienced computational linguists. S... |
Named-entity recognition : In 2001, research indicated that even state-of-the-art NER systems were brittle, meaning that NER systems developed for one domain did not typically perform well on other domains. Considerable effort is involved in tuning NER systems to perform well in a new domain; this is true for both rule... |
Named-entity recognition : Despite high F1 numbers reported on the MUC-7 dataset, the problem of named-entity recognition is far from being solved. The main efforts are directed to reducing the annotations labor by employing semi-supervised learning, robust performance across domains and scaling up to fine-grained enti... |
Named-entity recognition : Controlled vocabulary Coreference resolution Entity linking (aka named entity normalization, entity disambiguation) Information extraction Knowledge extraction Onomastics Record linkage Smart tag (Microsoft) == References == |
Neural machine translation : Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It is the dominant approach today: 293 : 1 and can produce transl... |
Neural machine translation : In the translation task, a sentence x = x 1 , I =x_ (consisting of I tokens x i ) in the source language is to be translated into a sentence y = x 1 , J =x_ (consisting of J tokens x j ) in the target language. The source and target tokens (which in the simple event are used for each ... |
Neural machine translation : NMT has overcome several challenges that were present in statistical machine translation (SMT): NMT's full reliance on continuous representation of tokens overcame sparsity issues caused by rare words or phrases. Models were able to generalize more effectively.: 1 : 900–901 The limited n-gr... |
Neural machine translation : As outlined in the history section above, instead of using an NMT system that is trained on parallel text, one can also prompt a generative LLM to translate a text. These models differ from an encoder-decoder NMT system in a number of ways:: 1 Generative language models are not trained on t... |
Neural machine translation : Koehn, Philipp (2020). Neural Machine Translation. Cambridge University Press. Stahlberg, Felix (2020). Neural Machine Translation: A Review and Survey. |
Neural machine translation : Attention (machine learning) Transformer (machine learning model) Seq2seq == References == |
Part-of-speech tagging : In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context. A simplified form of this is ... |
Part-of-speech tagging : Part-of-speech tagging is harder than just having a list of words and their parts of speech, because some words can represent more than one part of speech at different times, and because some parts of speech are complex. This is not rare—in natural languages (as opposed to many artificial langu... |
Part-of-speech tagging : Semantic net Sliding window based part-of-speech tagging Trigram tagger Word sense disambiguation |
Phrase chunking : Phrase chunking is a phase of natural language processing that separates and segments a sentence into its subconstituents, such as noun, verb, and prepositional phrases, abbreviated as NP, VP, and PP, respectively. Typically, each subconstituent or chunk is denoted by brackets. |
Phrase chunking : Terminology extraction Part-of-speech tagging Constituent (linguistics) |
Phrase chunking : TermExtractor TreeTagger Chunker == References == |
Relationship extraction : A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents. The task is very similar to that of information extraction (IE), but IE additionally requires the removal of repeated rela... |
Relationship extraction : The concept of relationship extraction was first introduced during the 7th Message Understanding Conference in 1998. Relationship extraction involves the identification of relations between entities and it usually focuses on the extraction of binary relations. Application domains where relatio... |
Relationship extraction : There are several methods used to extract relationships and these include text-based relationship extraction. These methods rely on the use of pretrained relationship structure information or it could entail the learning of the structure in order to reveal relationships. Another approach to th... |
Relationship extraction : Researchers have constructed multiple datasets for benchmarking relationship extraction methods. One such dataset was the document-level relationship extraction dataset called DocRED released in 2019. It uses relations from Wikidata and text from the English Wikipedia. The dataset has been use... |
Relationship extraction : Text analytics Semantic analytics Semantic role labeling Information extraction Business Intelligence == References == |
Résumé parsing : Resume parsing, also known as CV parsing, resume extraction, or CV extraction, allows for the automated storage and analysis of resume data. The resume is imported into parsing software and the information is extracted so that it can be sorted and searched. |
Résumé parsing : Resume parsers analyze a resume, extract the desired information, and insert the information into a database with a unique entry for each candidate. Once the resume has been analyzed, a recruiter can search the database for keywords and phrases and get a list of relevant candidates. Many parsers suppor... |
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