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Drush (DRUpal SHell) is a computer software shell-based application used to control, manipulate, and administer Drupal websites. == Details == Drush was originally developed by Arto Bendiken for Drupal 4.7. In May 2007, it was partly rewritten and redesigned for Drupal 5 by Franz Heinzmann. Drush is maintained by Moshe Weitzman with the support of Owen Barton, greg.1.anderson, jonhattan, Mark Sonnabaum, Jonathan Hedstrom and Christopher Gervais.
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In search of the best AI avatar generator? An AI avatar generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI avatar generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Shopping for the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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DeepL is a German AI research company known for its language AI platform, which includes DeepL Translator and DeepL Voice, and for DeepL Agent, an AI agent capable of planning workflows and using office systems and tools autonomously, in response to natural language instructions. Its algorithm uses the transformer architecture. It offers a paid subscription for additional features and access to its translation application programming interface. DeepL was founded in 2017 by Jaroslaw Kutylowski and is a unicorn, valued at $2 billion after a Series C funding round raised $300 million in May 2024. Its more than 200,000 business customers include a large proportion of the Fortune 500. == History == The translating system was first developed within Linguee by a team led by Chief Technology Officer Jarosław Kutyłowski in 2016. It was launched as DeepL Translator on 28 August 2017 and offered translations between English, German, French, Spanish, Italian, Polish and Dutch. At its launch, it claimed to have surpassed its competitors in blind tests and BLEU scores, including Google Translate, Amazon Translate, Microsoft Translator and Facebook's translation feature. With the release of DeepL in 2017, Linguee's company name was changed to DeepL GmbH, and it is also financed by advertising on its sister site, linguee.com. Support for Portuguese and Russian was added on 5 December 2018. In July 2019, Jarosław Kutyłowski became the CEO of DeepL GmbH and restructured the company into a Societas Europaea in 2021. Translation software for Microsoft Windows and macOS was released in September 2019. Support for Chinese (simplified) and Japanese was added on 19 March 2020, which the company claimed to have surpassed the aforementioned competitors as well as Baidu and Youdao. Then, 13 more European languages were added in March 2021: Bulgarian, Czech, Danish, Estonian, Finnish, Greek, Hungarian, Latvian, Lithuanian, Romanian, Slovak, Slovenian, and Swedish, bringing the total number of supported languages to 24. On 25 May 2022, support for Indonesian and Turkish was added, and support for Ukrainian was added on 14 September 2022. In January 2023, the company reached a valuation of 1 billion euro and became the most valued startup company in Cologne. At the end of the month, support for Korean and Norwegian (Bokmål) was also added. In May 2024, the company announced an investment of US$300 million at AI. In January 2026, more languages were supported, including Luxembourgish and Irish. == Services == === Translation method === The service uses a proprietary algorithm with convolutional neural networks (CNNs) that have been trained with the Linguee database. According to the developers, the service uses a newer improved architecture of neural networks, resulting in a more natural sound of translations than by competing services. The translation is generated using a supercomputer that reaches 5.1 petaflops and is operated in Iceland with hydropower. DeepL's data centers are located at the EcoDataCenter in Falun, Sweden, which is a data center for sustainability. In general, CNNs are slightly more suitable for long coherent word sequences, but they have so far not been used by the competition because of their weaknesses compared to recurrent neural networks. The weaknesses of DeepL are compensated for by supplemental techniques, some of which are publicly known. === Translator and subscription === The translator can be used for free with a maximum limit of 1,500 characters per translation. Microsoft Word and PowerPoint files in Office Open XML file formats (.docx and .pptx) and PDF files up to 5MB in size can also be translated. It offers paid subscription DeepL Pro, which has been available since March 2018 and includes application programming interface access and a software plug-in for computer-assisted translation tools, including SDL Trados Studio. Unlike the free version, translated texts are stated to not be saved on the server; also, the character limit is removed. The monthly pricing model includes a set amount of text, with texts beyond that being calculated according to the number of characters. ==== Supported languages ==== As of May 2026, the translation service supports the following languages: Additionally, these languages are currently in beta, indicated by an asterisk after their name in the language picker: === DeepL Write === In November 2022, DeepL launched a tool to improve monolingual texts in English and German, called DeepL Write. In December, the company removed access and informed journalists that it was only for internal use and that DeepL Write would be relaunched in early 2023. The public beta version was then released on January 17, 2023. In the summer of 2024, DeepL announced the availability of two more languages in DeepL Write: French and Spanish. By January 2024, DeepL had added an additional two: Portuguese (European and Brazilian) and Italian. === DeepL Agent === In November 2025, DeepL launched an AI agent called DeepL Agent which is capable of operating business applications in a human-like manner. == Reception == The reception of DeepL has been generally positive. TechCrunch appreciates it for the accuracy of its translations and stating that it was more accurate and nuanced than Google Translate. Le Monde thanks its developers for translating French text into more "French-sounding" expressions. RTL Z stated that DeepL Translator "offers better translations […] when it comes to Dutch to English and vice versa". La Repubblica, and a Latin American website, "WWWhat's new?", showed praise as well. A 2018 paper by the University of Bologna evaluated the Italian-to-German translation capabilities and found the preliminary results to be similar in quality to Google Translate. In September 2021, Slator remarked that the language industry response was more measured than the press and noted that DeepL is still highly regarded by users. A reviewer noted in 2018 that DeepL had far fewer languages available for translation than competing products. == Awards and honors == DeepL won the 2020 Webby Award for Best Practices and the 2020 Webby Award for Technical Achievement (Apps, Mobile, and Features), both in the category Apps, Mobile & Voice. In April 2025, DeepL was featured in the Forbes AI 50 list.
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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 trained with recordings of real speech. Tests with US English and Mandarin reportedly showed that the system outperforms Google's best existing text-to-speech (TTS) systems, although as of 2016 its text-to-speech synthesis still was less convincing than actual human speech. WaveNet's ability to generate raw waveforms means that it can model any kind of audio, including music. == History == 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 words. The most common of these is called concatenative TTS. It consists of large library of speech fragments, recorded from a single speaker that are then concatenated to produce complete words and sounds. The result sounds unnatural, with an odd cadence and tone. The reliance on a recorded library also makes it difficult to modify or change the voice. Another technique, known as parametric TTS, uses mathematical models to recreate sounds that are then assembled into words and sentences. The information required to generate the sounds is stored in the parameters of the model. The characteristics of the output speech are controlled via the inputs to the model, while the speech is typically created using a voice synthesiser known as a vocoder. This can also result in unnatural sounding audio. == Design and ongoing research == === Background === WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal as an input and synthesises an output one sample at a time. It does so by sampling from a softmax (i.e. categorical) distribution of a signal value that is encoded using μ-law companding transformation and quantized to 256 possible values. === Initial concept and results === According to the original September 2016 DeepMind research paper WaveNet: A Generative Model for Raw Audio, the network was fed real waveforms of speech in English and Mandarin. As these pass through the network, it learns a set of rules to describe how the audio waveform evolves over time. The trained network can then be used to create new speech-like waveforms at 16,000 samples per second. These waveforms include realistic breaths and lip smacks – but do not conform to any language. WaveNet is able to accurately model different voices, with the accent and tone of the input correlating with the output. For example, if it is trained with German, it produces German speech. The capability also means that if the WaveNet is fed other inputs – such as music – its output will be musical. At the time of its release, DeepMind showed that WaveNet could produce waveforms that sound like classical music. === Content (voice) swapping === According to the June 2018 paper Disentangled Sequential Autoencoder, DeepMind has successfully used WaveNet for audio and voice "content swapping": the network can swap the voice on an audio recording for another, pre-existing voice while maintaining the text and other features from the original recording. "We also experiment on audio sequence data. Our disentangled representation allows us to convert speaker identities into each other while conditioning on the content of the speech." (p. 5) "For audio, this allows us to convert a male speaker into a female speaker and vice versa [...]." (p. 1) According to the paper, a two-digit minimum amount of hours (c. 50 hours) of pre-existing speech recordings of both source and target voice are required to be fed into WaveNet for the program to learn their individual features before it is able to perform the conversion from one voice to another at a satisfying quality. The authors stress that "[a]n advantage of the model is that it separates dynamical from static features [...]." (p. 8), i. e. WaveNet is capable of distinguishing between the spoken text and modes of delivery (modulation, speed, pitch, mood, etc.) to maintain during the conversion from one voice to another on the one hand, and the basic features of both source and target voices that it is required to swap on the other. The January 2019 follow-up paper Unsupervised speech representation learning using WaveNet autoencoders details a method to successfully enhance the proper automatic recognition and discrimination between dynamical and static features for "content swapping", notably including swapping voices on existing audio recordings, in order to make it more reliable. Another follow-up paper, Sample Efficient Adaptive Text-to-Speech, dated September 2018 (latest revision January 2019), states that DeepMind has successfully reduced the minimum amount of real-life recordings required to sample an existing voice via WaveNet to "merely a few minutes of audio data" while maintaining high-quality results. Its ability to clone voices has raised ethical concerns about WaveNet's ability to mimic the voices of living and dead persons. According to a 2016 BBC article, companies working on similar voice-cloning technologies (such as Adobe Voco) intend to insert watermarking inaudible to humans to prevent counterfeiting, while maintaining that voice cloning satisfying, for instance, the needs of entertainment-industry purposes would be of a far lower complexity and use different methods than required to fool forensic evidencing methods and electronic ID devices, so that natural voices and voices cloned for entertainment-industry purposes could still be easily told apart by technological analysis. == Applications == 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 for US English and Japanese across all Google platforms. In November 2017, DeepMind researchers released a research paper detailing a proposed method of "generating high-fidelity speech samples at more than 20 times faster than real-time", called "Probability Density Distillation". At the annual I/O developer conference in May 2018, it was announced that new Google Assistant voices were available and made possible by WaveNet; WaveNet greatly reduced the number of audio recordings that were required to create a voice model by modeling the raw audio of the voice actor samples.
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Shopping for the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Round-trip translation (RTT), also known as back-and-forth translation, recursive translation and bi-directional translation, is the process of translating a word, phrase or text into another language (forward translation), then translating the result back into the original language (back translation), using machine translation (MT) software. It is often used by laypeople to evaluate a machine translation system, or to test whether a text is suitable for MT when they are unfamiliar with the target language. Because the resulting text can often differ substantially from the original, RTT can also be a source of entertainment. == Software quality == To compare the quality of different machine translation systems, users perform RTT and compare the resulting text to the original. The theory is that the closer the result of the RTT is to the original text, the higher the quality of the machine translation system. One of the problems with this technique is that if there is a problem with the resulting text it is impossible to know whether the error occurred in the forward translation, in the back translation, or in both. In addition, it is possible to get a good back translation from a bad forward translation. A study using the automatic evaluation methods BLEU and F-score compared five different free online translation programs, evaluating the quality of both the forward translation and the back translation, and found no correlation between the quality of the forward translation and the quality of the back translation (i.e., a high quality forward translation did not always correspond to a high quality back translation). The author concluded that RTT was a poor method of predicting the quality of machine translation software. This conclusion was reinforced by a more in-depth study also using automatic evaluation methods. A subsequent study which included human evaluation of the back translation in addition to automatic evaluation methods found that RTT might have some ability to predict the quality of a machine translation system not on a sentence-by-sentence basis but for larger texts. == Suitability of text for machine translation == It is also suggested that RTT can be used to determine whether a text is suitable for machine translation. The idea being that if RTT results in a text that is close to the original, the text is suitable for MT. If after using RTT, the resulting text is inaccurate, the source text can then be edited until a satisfactory result is achieved. One of the studies looking at RTT as a means of measuring MT system quality also looked at its ability to predict whether a text was suitable for machine translation. It found that using different types of text also did not result in any correlation between the quality of the forward translation and the quality of the back translation. In contrast another study using human evaluation found that there was a correlation between the quality of the forward translation and the back translation and that this correlation could be used to estimate the quality of the forward translation. This correlation could be used to estimate the quality of the forward translation and by simplifying the source text, improve the quality of the forward translation. == Entertainment == Although the use of RTT for assessing MT system quality or the suitability of a text for MT is in doubt, it is a way to have fun with machine translation. The text produced from an RTT can be comically bad. At one time websites existed for the sole purpose of performing RTT for fun. Other variations send the text through several languages before translating it back into the original or continue translating the text back and forth until it reaches equilibrium (i.e., the result of the back translation is identical to the text used for the forward translation). RTT as entertainment appeared in Philip K. Dick's novel Galactic Pot-Healer. The main character runs book titles and sayings through RTT then has his friends try to guess the original. The Australian television show Spicks and Specks had a contest called "Turning Japanese" which used RTT on song lyrics. Contestants needed to correctly guess the title of the song from which the lyrics were taken.
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Michael J. Collins (born 4 March 1970) is a researcher in the field of computational linguistics. He is the Vikram S. Pandit Professor of Computer Science at Columbia University. His research interests are in natural language processing as well as machine learning and he has made important contributions in statistical parsing and in statistical machine learning. In his studies Collins covers a wide range of topics such as parse re-ranking, tree kernels, semi-supervised learning, machine translation and exponentiated gradient algorithms with a general focus on discriminative models and structured prediction. One notable contribution is a state-of-the-art parser for the Penn Wall Street Journal corpus. As of 11 November 2015, his works have been cited 16,020 times, and he has an h-index of 47. Collins worked as a researcher at AT&T Labs between January 1999 and November 2002, and later held the positions of assistant and associate professor at M.I.T. Since January 2011, he has been a professor at Columbia University. In 2011, he was named a fellow of the Association for Computational Linguistics.
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Neurocomputing is a peer-reviewed scientific journal covering research on artificial intelligence, machine learning, and neural computation. It was established in 1989 and is published by Elsevier. The editor-in-chief is Zidong Wang (Brunel University London). Independent scientometric studies noted that despite being one of the most productive journals in the field, it has kept its reputation across the years intact and plays an important role in leading the research in the area. The journal is abstracted and indexed in Scopus and Science Citation Index Expanded. According to the Journal Citation Reports, its 2023 impact factor is 5.5.
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In mathematics and computer science, the probabilistic automaton (PA) is a generalization of the nondeterministic finite automaton; it includes the probability of a given transition into the transition function, turning it into a transition matrix. Thus, the probabilistic automaton also generalizes the concepts of a Markov chain and of a subshift of finite type. The languages recognized by probabilistic automata are called stochastic languages; these include the regular languages as a subset. The number of stochastic languages is uncountable. The concept was introduced by Michael O. Rabin in 1963; a certain special case is sometimes known as the Rabin automaton (not to be confused with the subclass of ω-automata also referred to as Rabin automata). In recent years, a variant has been formulated in terms of quantum probabilities, the quantum finite automaton. == Informal Description == For a given initial state and input character, a deterministic finite automaton (DFA) has exactly one next state, and a nondeterministic finite automaton (NFA) has a set of next states. A probabilistic automaton (PA) instead has a weighted set (or vector) of next states, where the weights must sum to 1 and therefore can be interpreted as probabilities (making it a stochastic vector). The notions states and acceptance must also be modified to reflect the introduction of these weights. The state of the machine as a given step must now also be represented by a stochastic vector of states, and a state accepted if its total probability of being in an acceptance state exceeds some cut-off. A PA is in some sense a half-way step from deterministic to non-deterministic, as it allows a set of next states but with restrictions on their weights. However, this is somewhat misleading, as the PA utilizes the notion of the real numbers to define the weights, which is absent in the definition of both DFAs and NFAs. This additional freedom enables them to decide languages that are not regular, such as the p-adic languages with irrational parameters. As such, PAs are more powerful than both DFAs and NFAs (which are famously equally powerful). == Formal Definition == The probabilistic automaton may be defined as an extension of a nondeterministic finite automaton ( Q , Σ , δ , q 0 , F ) {\displaystyle (Q,\Sigma ,\delta ,q_{0},F)} , together with two probabilities: the probability P {\displaystyle P} of a particular state transition taking place, and with the initial state q 0 {\displaystyle q_{0}} replaced by a stochastic vector giving the probability of the automaton being in a given initial state. For the ordinary non-deterministic finite automaton, one has a finite set of states Q {\displaystyle Q} a finite set of input symbols Σ {\displaystyle \Sigma } a transition function δ : Q × Σ → ℘ ( Q ) {\displaystyle \delta :Q\times \Sigma \to \wp (Q)} a set of states F {\displaystyle F} distinguished as accepting (or final) states F ⊆ Q {\displaystyle F\subseteq Q} . Here, ℘ ( Q ) {\displaystyle \wp (Q)} denotes the power set of Q {\displaystyle Q} . By use of currying, the transition function δ : Q × Σ → ℘ ( Q ) {\displaystyle \delta :Q\times \Sigma \to \wp (Q)} of a non-deterministic finite automaton can be written as a membership function δ : Q × Σ × Q → { 0 , 1 } {\displaystyle \delta :Q\times \Sigma \times Q\to \{0,1\}} so that δ ( q , a , q ′ ) = 1 {\displaystyle \delta (q,a,q^{\prime })=1} if q ′ ∈ δ ( q , a ) {\displaystyle q^{\prime }\in \delta (q,a)} and 0 {\displaystyle 0} otherwise. The curried transition function can be understood to be a matrix with matrix entries [ θ a ] q q ′ = δ ( q , a , q ′ ) {\displaystyle \left[\theta _{a}\right]_{qq^{\prime }}=\delta (q,a,q^{\prime })} The matrix θ a {\displaystyle \theta _{a}} is then a square matrix, whose entries are zero or one, indicating whether a transition q → a q ′ {\displaystyle q{\stackrel {a}{\rightarrow }}q^{\prime }} is allowed by the NFA. Such a transition matrix is always defined for a non-deterministic finite automaton. The probabilistic automaton replaces these matrices by a family of right stochastic matrices P a {\displaystyle P_{a}} , for each symbol a in the alphabet Σ {\displaystyle \Sigma } so that the probability of a transition is given by [ P a ] q q ′ {\displaystyle \left[P_{a}\right]_{qq^{\prime }}} A state change from some state to any state must occur with probability one, of course, and so one must have ∑ q ′ [ P a ] q q ′ = 1 {\displaystyle \sum _{q^{\prime }}\left[P_{a}\right]_{qq^{\prime }}=1} for all input letters a {\displaystyle a} and internal states q {\displaystyle q} . The initial state of a probabilistic automaton is given by a row vector v {\displaystyle v} , whose components are the probabilities of the individual initial states q {\displaystyle q} , that add to 1: ∑ q [ v ] q = 1 {\displaystyle \sum _{q}\left[v\right]_{q}=1} The transition matrix acts on the right, so that the state of the probabilistic automaton, after consuming the input string a b c {\displaystyle abc} , would be v P a P b P c {\displaystyle vP_{a}P_{b}P_{c}} In particular, the state of a probabilistic automaton is always a stochastic vector, since the product of any two stochastic matrices is a stochastic matrix, and the product of a stochastic vector and a stochastic matrix is again a stochastic vector. This vector is sometimes called the distribution of states, emphasizing that it is a discrete probability distribution. Formally, the definition of a probabilistic automaton does not require the mechanics of the non-deterministic automaton, which may be dispensed with. Formally, a probabilistic automaton PA is defined as the tuple ( Q , Σ , P , v , F ) {\displaystyle (Q,\Sigma ,P,v,F)} . A Rabin automaton is one for which the initial distribution v {\displaystyle v} is a coordinate vector; that is, has zero for all but one entries, and the remaining entry being one. == Stochastic languages == The set of languages recognized by probabilistic automata are called stochastic languages. They include the regular languages as a subset. Let F = Q accept ⊆ Q {\displaystyle F=Q_{\text{accept}}\subseteq Q} be the set of "accepting" or "final" states of the automaton. By abuse of notation, Q accept {\displaystyle Q_{\text{accept}}} can also be understood to be the column vector that is the membership function for Q accept {\displaystyle Q_{\text{accept}}} ; that is, it has a 1 at the places corresponding to elements in Q accept {\displaystyle Q_{\text{accept}}} , and a zero otherwise. This vector may be contracted with the internal state probability, to form a scalar. The language recognized by a specific automaton is then defined as L η = { s ∈ Σ ∗ | v P s Q accept > η } {\displaystyle L_{\eta }=\{s\in \Sigma ^{}\vert vP_{s}Q_{\text{accept}}>\eta \}} where Σ ∗ {\displaystyle \Sigma ^{}} is the set of all strings in the alphabet Σ {\displaystyle \Sigma } (so that is the Kleene star). The language depends on the value of the cut-point η {\displaystyle \eta } , normally taken to be in the range 0 ≤ η < 1 {\displaystyle 0\leq \eta <1} . A language is called η-stochastic if and only if there exists some PA that recognizes the language, for fixed η {\displaystyle \eta } . A language is called stochastic if and only if there is some 0 ≤ η < 1 {\displaystyle 0\leq \eta <1} for which L η {\displaystyle L_{\eta }} is η-stochastic. A cut-point is said to be an isolated cut-point if and only if there exists a δ > 0 {\displaystyle \delta >0} such that | v P ( s ) Q accept − η | ≥ δ {\displaystyle \vert vP(s)Q_{\text{accept}}-\eta \vert \geq \delta } for all s ∈ Σ ∗ {\displaystyle s\in \Sigma ^{}} == Properties == Every regular language is stochastic, and more strongly, every regular language is η-stochastic. A weak converse is that every 0-stochastic language is regular; however, the general converse does not hold: there are stochastic languages that are not regular. Every η-stochastic language is stochastic, for some 0 < η < 1 {\displaystyle 0<\eta <1} . Every stochastic language is representable by a Rabin automaton. If η {\displaystyle \eta } is an isolated cut-point, then L η {\displaystyle L_{\eta }} is a regular language. == p-adic languages == The p-adic languages provide an example of a stochastic language that is not regular, and also show that the number of stochastic languages is uncountable. A p-adic language is defined as the set of strings L η ( p ) = { n 1 n 2 n 3 … | 0 ≤ n k < p and 0. n 1 n 2 n 3 … > η } {\displaystyle L_{\eta }(p)=\{n_{1}n_{2}n_{3}\ldots \vert 0\leq n_{k}
Comparing the best AI art generator? An AI art generator is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI art generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Law and corpus linguistics (LCL) is an academic sub-discipline that uses large databases of examples of language usage equipped with tools designed by linguists called corpora to better get at the meaning of words and phrases in legal texts (statutes, constitutions, contracts, etc.). Thus, LCL is the application of corpus linguistic tools, theories, and methodologies to issues of legal interpretation in much the same way law and economics is the application of economic tools, theories, and methodologies to various legal issues. == History == A 2005 law review article by Lawrence Solan noted in passing that corpus linguistics had potential for its application to interpreting legal texts. But the first systematic exploration and advocacy of applying the tools and methodologies of corpus linguistics to legal interpretive questions of law and corpus linguistics came in the fall of 2010, when the BYU Law Review published a note by Stephen Mouritsen, entitled The Dictionary is Not a Fortress: Definitional Fallacies and a Corpus-Based Approach to Plain Meaning. The note argued that dictionaries are the primary linguistic tool used by judges to determine the plain or ordinary meaning of words and phrases, and highlighted the deficiencies of such an approach. In its stead, the note proposed using corpus linguistics. And the note would be later cited by Adam Liptak in a New York Times article on statutory construction. Law and corpus linguistics (LCL) gained greater legitimacy in July 2011 with the first judicial opinion in American history utilizing corpus linguistics to determine the meaning of a legal text: In re the Adoption of Baby E.Z. In a concurrence in part and in the judgment, Justice Thomas Lee wrote to put forth an alternative ground for the majority's holding—interpreting the phrase "custody determination" by using corpus linguistics. Justice Lee looked at 500 randomized sample sentences from the Corpus of Contemporary American English (COCA) and found that the most common sense of "custody" was in the context of divorce rather than adoption. Further, he found that "custody" is ten times more likely to co-occur (or collocate) with "divorce" than with "adoption". From that evidence Justice Lee concluded that he "would find that the custody proceedings covered by the Act are limited to proceedings resulting in the modifiable custody orders of a divorce", rather than the broader range of custody proceedings. Other jurisprudence and scholarship would follow. In a 2015 concurrence in State v. Rasabout, Justice Lee used a COCA search to determine that "discharge" when used with a firearm (or one of its synonyms) overwhelmingly referred to a single shot rather than emptying the entire magazine of the weapon. And in 2016, four of the five justices joined a footnote in a majority opinion by Justice Lee commending a party for using corpus linguistics in its briefing even though the Court found it unnecessary to resolve the related question. Finally, in 2016 the Michigan Supreme Court became the first court to use a linguist-designed corpus in a majority opinion (COCA), with both the majority and the dissent turning to COCA to determine the meaning of the word "information". In 2020, courts desiring to bolster the legal theory of original intent have sought the opportunity to undertake analyses of statutes utilizing corpus linguistics. In a Ninth Circuit Court of Appeals case, Jones v. Becerra (No. 20-56174), a case involving the Second Amendment and the constitutionality of a California statute which bans the sale of firearms to individuals under the age of 21, a Ninth Circuit panel requested that the parties address three questions: 1) “What is the original public meaning of the Second Amendment phrases: ‘A well regulated Militia’; ‘the right of the people’; and ‘shall not be infringed’? 2) How does the tool of corpus linguistics help inform the determination of the original public meaning of those Second Amendment phrases?” 3) How do the data yielded from corpus linguistics assist in the interpretation of the constitutionality of age-based restrictions under the Second Amendment? As to scholarship, in 2012, Mouritsen followed up his original work with an article in the Columbia Science and Technology Law Review, where he further refined and promoted the use of corpus-based methods for determining questions of legal ambiguity. Additionally, in 2016 two essays and an article on law and corpus linguistics were published. The Yale Law Journal Forum published Corpus Linguistics & Original Public Meaning: A New Tool to Make Originalism More Empirical. Written by Justice Lee and two co-authors, the essay urged originalists to turn to corpus linguistics to improve the rigor and accuracy of originalist scholarship. And in response, the Forum published an essay by Lawrence Solan (a Brooklyn Law professor with a PhD in linguistics), Can Corpus Linguistics Help Make Originalism Scientific? The Boston University Public Interest Law Journal published The Merciful Corpus: The Rule of Lenity, Ambiguity and Corpus Linguistics by Daniel Ortner. In the article Ortner applied corpus linguistics to determining whether sufficient ambiguity exists to trigger the rule of lenity in five Supreme Court cases. Looking forward, in 2017 two more articles are slated for publication. Lee Strang focuses on corpus linguistics and originalism in the U.C. Davis Law Review, and Lawrence Solan and Tammy Gales explore corpus linguistics in the context of finding ordinary meaning in statutory interpretation in the International Journal of Legal Discourse. Lawyers and journalists have also taken notice of corpus linguistics at it relates to the law. In 2010, Neal Goldfarb filed the first known brief in the Supreme Court using corpus linguistics (COCA) to determine whether the ordinary meaning of "personal" referred to corporations in the case FCC v. AT&T. The amicus brief looked at the top collocates (words that co-occur) of "personal" in COHA as well as BYU's Time Magazine Corpus. And writing for The Atlantic, Ben Zimmer took note of this new trend, referring to corpus linguistics in the courts as "Like Lexis on Steroids". On the academic front, in 2013 BYU Law School started the first class on law and corpus linguistics, co-taught by Mouritsen, Lee, and (now Dean) Gordon Smith. The class is currently in its fourth year. And in February 2016, BYU Law School hosted the inaugural conference on LCL, with over two dozen legal and linguistic scholars from around the country discussing and debating the next steps forward for the growing academic movement. The conference has been held regularly in subsequent years. At the 2016 conference BYU Law School announced its plans and progress on the Corpus of Founding Era American English (COFEA), a corpus that covers 1760–1799 and contains more than 120 million words have been collected from founding era letters, diaries, newspapers, non-fiction books, fiction, sermons, speeches, debates, legal cases, and other legal materials.
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In art, craft, and engineering, masking is the use of materials to protect areas from change, or to focus change on other areas. This can describe either the techniques and materials used to control the development of a work of art by protecting a desired area from change; or a phenomenon that (either intentionally or unintentionally) causes a sensation to be concealed from conscious attention. The term is derived from the word mask, in the sense that it hides the face from view. == In painting == Masking materials supplement a painter's dexterity and choice of applicator to control where paint is laid. Examples include the use of a stencil or masking tape to protect areas which are not to be painted. === Solid masks === Most solid masks require an adhesive to hold the mask in place while work is performed. Some, such as masking tape and frisket, come with adhesive pre-applied. Solid masks are readily available in bulk, and are used in large painting jobs. Paper products Kraft paper Butcher paper Masking tape Plastic film Frisket Polyester tape Stencils Silk screen === Liquid masks === Liquid masks are preferred where precision is needed; they prevent paint from seeping underneath, resulting in clean edges. Care must be taken to remove them without damaging the work underneath. Latex or other polymers Molten wax Gesso, typically a substrate for painting, but can also be applied to achieve masking effects == In photography == Masks used for photography are used to enhance the quality of an image. Representations of a scene—whether film, video display, or printed—do not have the dynamic contrast range available to the human eye looking directly at the same scene. Adjusting the contrast in an image helps restore some of the perceived qualities of the original scene. These adjustments are typically performed on "blown-out" highlights, and "crushed" or "muddy" shadow areas, where clipping has occurred; or on desaturated colors. Photographic masks are peculiar in that they are produced from the image they will alter, an exercise in recursion. Masks used to produce other effects are similar to those used in painting. === Controlling exposure === ==== Film ==== The basic methods of controlling exposure are dodging and burning, which respectively lighten (reduce exposure) and darken (increase exposure) areas of an image. The tools a film photographer uses range from shaped pieces of black material (such as studio foil, foam, and paper) to the photographer's hands. To create a photographic mask, a sheet of negative film is contact-exposed to the original film negative or slide positive in a particular way. Both films are then combined to produce a processed positive. The process is similar when applied using digital techniques: the inverse of the working image is reduced to an image mask; filters or other adjustments are then applied, using the mask to selectively block portions of the image. ==== Digital ==== Image editors offer at the very least a "Select All" command and a rectangular "marquee" selection tool. (The word "marquee" describes the "crawling ants" border used to highlight the active region.) Once a selection is created, further changes to the image will be confined to that area. To continue editing the rest of the image, the selection is either "deselected" or the entire image is selected. Advanced suites offer more ways to select portions of an image, as well as ways to combine these selections through. Selection masks can be switched between an editable greyscale image and a mask. They allow the user to create a mask using the suite's painting tools. === Contrast masking === When the contrast range of an image needs to be adjusted, a contrast mask is a simple solution. The processed image resembles what would be achieved when exposing through a neutral density filter, but the effects are focused highly upon the extreme regions of the image. The blocking areas of the mask coincide with the highlights of the image, and the permissive areas with the shadows, resulting in more detail appearing in each. ==== Film ==== The mask is often made from high-quality black-and-white film, such as Kodak Technical Pan, which allows for a degree of softening on the mask. Its processing time is reduced so as to not completely oppose the original negative. Both negatives are combined and registered, and collectively exposed with additional time to compensate for the presence of the mask. ==== Digital ==== Contrast masking is made simpler with digital editing. A grayscale version of the image is produced, either by desaturation or by calculating selected ratios of the image's color channels, inverted, and blurred. The mask and original image are blended together to produce the final processed image. Some image editors allow for refinement of the effect by changing the strength of the blend. Contrast masking can be considered to be the opposite of gamma correction, which adjusts the midtones of an image. Effects similar to contrast masking can be achieved by adjusting the response curves of an image. === Unsharp masking === A derivative of contrast masking is unsharp masking, an unusual term for a process intended to increase the apparent sharpness (acutance) of an image. Unsharp masking uses a blurred form of the image to increase contrast along regions of moderate contrast difference. Around edges, the blur region causes highlights to overexpose and shadows to underexpose. Taken to an extreme, the edges become overly visible and detract from the quality of the image—this is referred to as halation. Unsharp masking does not increase the actual sharpness, as it cannot recover details lost to blurring. ==== Film ==== Unsharp masking allows the photographer to sharpen areas that have become blurred in the original negative, due to long shutter speed/exposure time, or from using a wide aperture/"fast" lens. When creating the unsharp mask, extra space or diffusing material is added between the image and the mask to produce the necessary blur. ==== Digital ==== Unsharp masking has become automated in digital editing, with higher-end suites offering the process as a "tool" or "filter" in their standard sharpening kits—the actual creation of a mask is bypassed in favor of calculations that represent the mask's effect. The process depends on three factors: the radius of the blur, the strength of the effect, and the threshold degree of contrast above which the effect will be applied. (Adjusting the threshold allows the editor to apply the effect selectively upon moderately defined edges and ignore image noise.) Unsharp masking is computationally more complex than other sharpening algorithms, but results in a higher-quality remedy. Deconvolution allows for truer sharpening, but is much more complex than unsharp masking.
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Heng Ji is a computer scientist who works on information extraction and natural language processing. She is well known for her work on joined named entity recognition and relation extraction, as well as for her work on cross-document event extraction. She has been coordinating the popular NIST TAC Knowledge Base Population task since 2010. She has been recognised as one of AI's 10 to watch by IEEE Intelligent Systems in 2013, and has won multiple awards, including a NSF Career Award in 2009, Google Research awards in 2009 and 2014, and an IBM Watson Faculty Award in 2012. == Education == Heng Ji obtained a Bachelor's and master's degree in Computational Linguistics from Tsinghua University. She subsequently obtained a MSc, then PhD in Computer Science from New York University in 2008 under the supervision of Ralph Grishman. Her PhD thesis was on the topic of information extraction, with a particular focus on joint training of multiple components in the information extraction pipeline, as well as cross-lingual learning. == Career == Upon graduating with a PhD from New York University, Ji took up a position as assistant professor at Queens College, City University of New York, where she founded the BLENDER Lab, which focuses on research on cross-lingual, cross-documents, cross-media information extraction and fusion. In 2013, she joined Rensselaer Polytechnic Institute as an Edward P. Hamilton Development Chair and Tenured associate professor in Computer Science. Since 2019, she has been a full professor at the University of Illinois at Urbana–Champaign, as well as an Amazon Scholar. == Research == Heng Ji works in the area of natural language processing, machine learning and information extraction. She has published over 300 peer-reviewed research papers. Her work is published in the proceedings of computer science conferences, including the Annual Meeting of the Association for Computational Linguistics, The Web Conference, and the ACM Conference on Knowledge Discovery and Data Mining (KDD). Ji is a leading researcher in information extraction, having coordinated the popular NIST TAC Knowledge Base Population shared task since 2010. She is most recognised for her work on modelling interactions between subtasks in information extraction, which was also the topic of her PhD thesis, and for her work on event detection using cross-document signals. == Selected honors and distinctions == 2009 NSF Career Award 2009 Google Research Award 2012 IBM Watson Faculty Award 2013 IEEE AI's 10 to Watch 2014 Google Research Award 2016 World Economic Forum, 'Young Scientist' 2017 World Economic Forum, 'Young Scientist' 2020 Annual Meeting of the Association for Computational Linguistics, best demonstration paper
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In natural language processing, a topic model is a type of probabilistic, neural, or algebraic model for discovering the abstract topics that occur in a collection of documents. Topic modeling is a frequently used text mining tool for discovering hidden semantic features and structures in a text. The topics produced by topic models are generated through a variety of mathematical frameworks, including probabilistic generative models, matrix factorization methods based on word co-occurrence, and clustering algorithms applied to semantic embeddings. Topic models are commonly used to organize and discover latent features in large collections of unstructured text and other forms of big data. Beyond text mining, topic models have also been used to uncover latent structures in fields such as genetic information, bioinformatics, computer vision, and social networks. == History == An early topic model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998. Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999. Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization of PLSA. Developed by David Blei, Andrew Ng, and Michael I. Jordan in 2002, LDA introduces sparse Dirichlet prior distributions over document-topic and topic-word distributions, encoding the intuition that documents cover a small number of topics and that topics often use a small number of words. Other topic models are generally extensions on LDA, such as Pachinko allocation, which improves on LDA by modeling correlations between topics in addition to the word correlations which constitute topics. Hierarchical latent tree analysis (HLTA) is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics. == Topic models for context information == Approaches for temporal information include Block and Newman's determination of the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Griffiths & Steyvers used topic modeling on abstracts from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001 whereas Lamba & Madhusushan used topic modeling on full-text research articles retrieved from DJLIT journal from 1981 to 2018. In the field of library and information science, Lamba & Madhusudhan applied topic modeling on different Indian resources like journal articles and electronic theses and resources (ETDs). Nelson has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829 to 2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time. Yin et al. introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference. Chang and Blei included network information between linked documents in the relational topic model, to model the links between websites. The author-topic model by Rosen-Zvi et al. models the topics associated with authors of documents to improve the topic detection for documents with authorship information. HLTA was applied to a collection of recent research papers published at major AI and Machine Learning venues. The resulting model is called The AI Tree. The resulting topics are used to index the papers at aipano.cse.ust.hk to help researchers track research trends and identify papers to read, and help conference organizers and journal editors identify reviewers for submissions. To improve the qualitative aspects and coherency of generated topics, some researchers have explored the efficacy of "coherence scores", or otherwise how computer-extracted clusters (i.e. topics) align with a human benchmark. Coherence scores are metrics for optimising the number of topics to extract from a document corpus. == Algorithms == In practice, researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A survey by D. Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms with provable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics. Since 2017, neural networks has been leveraged in topic modeling in order to improve the speed of inference, and leading to further advancements like vONTSS, which allows humans to incorporate domain knowledge via weakly supervised learning. In 2018, a new approach to topic models was proposed based on the stochastic block model. Topic modeling has leveraged LLMs through contextual embedding and fine tuning. == Applications of topic models == === To quantitative biomedicine === Topic models are being used also in other contexts. For examples uses of topic models in biology and bioinformatics research emerged. Recently topic models has been used to extract information from dataset of cancers' genomic samples. In this case topics are biological latent variables to be inferred. === To analysis of music and creativity === Topic models can be used for analysis of continuous signals like music. For instance, they were used to quantify how musical styles change in time, and identify the influence of specific artists on later music creation.
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