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Vitruvian Man
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Vitruvian Man
Artist
Leonardo da Vinci
Year
c. 1490
Type
Pen and ink with wash over metalpoint
on paper
Dimensions
34.6 cm × 25.5 cm (13.6 in × 10.0 in)
Location
Gallerie dell'Accademia, Venice
The Vitruvian Man (Italian: Le proporzioni del corpo umano secondo Vitruvio, which is translated to "The proportions of the human body according to Vitruvius"), or simply L'Uomo Vitruviano (Italian pronunciation: [ˈlwɔːmo vitruˈvjaːno]), is a drawing by Leonardo da Vinci around 1490.[1] It is accompanied by notes based on the work of the architect Vitruvius. The drawing, which is in pen and ink on paper, depicts a man in two superimposed positions with his arms and legs apart and inscribed in a circle and square. The drawing and text are sometimes called the Canon of Proportions or, less often, Proportions of Man. It is kept in the Gabinetto dei disegni e stampe of the Gallerie dell'Accademia, in Venice, Italy, under reference 228. Like most works on paper, it is displayed to the public only occasionally, so it isn't part of the normal exhibition of the museum. [2][3]
The drawing is based on the correlations of ideal human proportions with geometry described by the ancient Roman architect Vitruvius in Book III of his treatise De architectura. Vitruvius described the human figure as being the principal source of proportion among the classical orders of architecture. Vitruvius determined that the ideal body should be eight heads high. Leonardo's drawing is traditionally named in honor of the architect.
Contents
[hide]
1 Subject and title
2 Evidence of collaboration
3 Legacy
4 See also
5 Notes
6 References
7 Sources
8 External links
Subject and title[edit]
This image demonstrates the blend of mathematics and art during the Renaissance and demonstrates Leonardo's deep understanding of proportion. In addition, this picture represents a cornerstone of Leonardo's attempts to relate man to nature. Encyclopædia Britannica online states, "Leonardo envisaged the great picture chart of the human body he had produced through his anatomical drawings and Vitruvian Man as a cosmografia del minor mondo (cosmography of the microcosm). He believed the workings of the human body to be an analogy for the workings of the universe."[4]
According to Leonardo's preview in the accompanying text, written in mirror writing, it was made as a study of the proportions of the (male) human body as described in Vitruvius. The text is in two parts, above[a] and below[b] the image.
The first paragraph of the upper part reports Vitruvius: "Vetruvio, architect, puts in his work on architecture that the measurements of man are in nature distributed in this manner, that is:
a palm is four fingers
a foot is four palms
a cubit is six palms
four cubits make a man
a pace is four cubits
a man is 24 palms
and these measurements are in his buildings". The second paragraph reads: "if you open your legs enough that your head is lowered by one-fourteenth of your height and raise your hands enough that your extended fingers touch the line of the top of your head, know that the centre of the extended limbs will be the navel, and the space between the legs will be an equilateral triangle".
The lower section of text gives these proportions:
the length of the outspread arms is equal to the height of a man
from the hairline to the bottom of the chin is one-tenth of the height of a man
from below the chin to the top of the head is one-eighth of the height of a man
from above the chest to the top of the head is one-sixth of the height of a man
from above the chest to the hairline is one-seventh of the height of a man.
the maximum width of the shoulders is a quarter of the height of a man.
from the breasts to the top of the head is a quarter of the height of a man.
the distance from the elbow to the tip of the hand is a quarter of the height of a man.
the distance from the elbow to the armpit is one-eighth of the height of a man.
the length of the hand is one-tenth of the height of a man.
the root of the penis is at half the height of a man.
the foot is one-seventh of the height of a man.
from below the foot to below the knee is a quarter of the height of a man.
from below the knee to the root of the penis is a quarter of the height of a man.
the distances from below the chin to the nose and the eyebrows and the hairline are equal to the ears and to one-third of the face.
The points determining these proportions are marked with lines on the drawing. Below the drawing itself is a single line equal to a side of the square and divided into four cubits, of which the outer two are divided into six palms each, two of which have the mirror-text annotation "palmi"; the outermost two palms are divided into four fingers each, and are each annotated "diti".
Leonardo is clearly illustrating Vitruvius' De architectura 3.1.2-3 which reads:
For the human body is so designed by nature that the face, from the chin to the top of the forehead and the lowest roots of the hair, is a tenth part of the whole height; the open hand from the wrist to the tip of the middle finger is just the same; the head from the chin to the crown is an eighth, and with the neck and shoulder from the top of the breast to the lowest roots of the hair is a sixth; from the middle of the breast to the summit of the crown is a fourth. If we take the height of the face itself, the distance from the bottom of the chin to the under side of the nostrils is one third of it; the nose from the under side of the nostrils to a line between the eyebrows is the same; from there to the lowest roots of the hair is also a third, comprising the forehead. The length of the foot is one sixth of the height of the body; of the forearm, one fourth; and the breadth of the breast is also one fourth. The other members, too, have their own symmetrical proportions, and it was by employing them that the famous painters and sculptors of antiquity attained to great and endless renown.
Similarly, in the members of a temple there ought to be the greatest harmony in the symmetrical relations of the different parts to the general magnitude of the whole. Then again, in the human body the central point is naturally the navel. For if a man be placed flat on his back, with his hands and feet extended, and a pair of compasses centred at his navel, the fingers and toes of his two hands and feet will touch the circumference of a circle described therefrom. And just as the human body yields a circular outline, so too a square figure may be found from it. For if we measure the distance from the soles of the feet to the top of the head, and then apply that measure to the outstretched arms, the breadth will be found to be the same as the height, as in the case of plane surfaces which are perfectly square.[5]
Leonardo's drawing combines a careful reading of the ancient text with his own observation of actual human bodies. In drawing the circle and square he correctly observes that the square cannot have the same centre as the circle,[6] the navel, but is somewhat lower in the anatomy. This adjustment is the innovative part of Leonardo's drawing and what distinguishes it from earlier illustrations. He also departs from Vitruvius by drawing the arms raised to a position in which the fingertips are level with the top of the head, rather than Vitruvius's much lower angle, in which the arms form lines passing through the navel.
The drawing itself is often used as an implied symbol of the essential symmetry of the human body, and by extension, the symmetry of the universe as a whole.[7]
It may be noticed by examining the drawing that the combination of arm and leg positions actually creates sixteen different poses. The pose with the arms straight out and the feet together is seen to be inscribed in the superimposed square. On the other hand, the "spread-eagle" pose is seen to be inscribed in the superimposed circle.
The drawing was purchased from Gaudenzio de' Pagave by Giuseppe Bossi,[8] who described, discussed and illustrated it in his monograph on Leonardo's The Last Supper, Del Cenacolo di Leonardo da Vinci libri quattro (1810).[9] The following year he excerpted the section of his monograph concerned with the Vitruvian Man and published it as Delle opinioni di Leonardo da Vinci intorno alla simmetria de'Corpi Umani (1811), with a dedication to his friend Antonio Canova.[10]
After Bossi's death in 1815 the Vitruvian Man was acquired in 1822, along with a number of his drawings, by the Gallerie dell'Accademia in Venice, Italy, and has remained there since.[11]
Evidence of collaboration[edit]
Evidence has been found that Leonardo might have been influenced by the work of Giacomo Andrea de Ferrara, a Renaissance architect, an expert on Vitruvius and a close friend.[12] Giacomo Andrea's original drawing has only one set of arms and legs, but Leonardo's has the position of his man's arms and legs change.[13]
Another possible influence for Leonardo's depiction may have been the codex depictions of human proportions in architecture by Francesco di Giorgio, a Sienese architect who compiled, in 1470, an unpublished treatise on civil and military architecture (Trattato di Architettura Civile e Militare).[citation needed]
Figures from Francesco di Giorgio's treatise
Vitruvian man.
Church Design
Castle Design
Legacy[edit]
Vitruvian Man has stimulated artists including Cesare Caesariano (1521), Albrecht Dürer (1528), Pietro di Giacomo Cataneo (1554), Heinrich Lautensack (1618), and William Blake (1795).
See also[edit]
Anthropometry
Body proportions
Leonardo's robot
Modulor
Notes[edit]
Jump up ^ Above the image:
Vetruvio, architecto, mecte nella sua op(er)a d'architectura, chelle misure dell'omo sono dalla natura
disstribuite inquessto modo cioè che 4 diti fa 1 palmo, et 4 palmi fa 1 pie, 6 palmi fa un chubito, 4
cubiti fa 1 homo, he 4 chubiti fa 1 passo, he 24 palmi fa 1 homo ecqueste misure son ne' sua edifiti.
Settu ap(r)i ta(n)to le ga(m)be chettu chali da chapo 1/14 di tua altez(z)a e ap(r)i e alza tanto le b(r)acia che cholle lunge dita tu tochi la linia della
somita del chapo, sappi che 'l cie(n)tro delle stremita delle
ap(er)te me(m)bra fia il bellicho. Ello spatio chessi truova infralle ga(m)be fia tria(n)golo equilatero
Jump up ^ Below the image:
Tanto ap(r)e l'omo nele b(r)accia, qua(n)to ella sua alteza.
Dal nasscimento de chapegli al fine di sotto del mento è il decimo dell'altez(z)a del(l)'uomo. Dal di socto del mento alla som(m)i-
tà del chapo he l'octavo dell'altez(z)a dell'omo. Dal di sop(r)a del pecto alla som(m)ità del chapo fia il sexto dell'omo. Dal di so-
p(r)a del pecto al nasscime(n)to de chapegli fia la sectima parte di tucto l'omo. Dalle tette al di sop(r)a del chapo fia
la quarta parte dell'omo. La mag(g)iore larg(h)ez(z)a delle spalli chontiene insè [la oct] la quarta parte dell'omo. Dal go-
mito alla punta della mano fia la quarta parte dell'omo, da esso gomito al termine della isspalla fia la octava
parte d'esso omo; tucta la mano fia la decima parte dell'omo. Il menb(r)o birile nasscie nel mez(z)o dell'omo. Il
piè fia la sectima parte dell'omo. Dal di socto del piè al di socto del ginochio fia la quarta parte dell'omo.
Dal di socto del ginochio al nasscime(n)to del memb(r)o fia la quarta parte dell'omo. Le parti chessi truovano infra
il me(n)to e 'l naso e 'l nasscime(n)to de chapegli e quel de cigli ciasscuno spatio p(er)se essimile alloreche è 'l terzo del volto
References[edit]
Jump up ^ The Secret Language of the Renaissance - Richard Stemp
Jump up ^ "The Vitruvian man". Leonardodavinci.stanford.edu. Retrieved 2010-08-20.
Jump up ^ "Da Vinci's Code". Witcombe.sbc.edu. Retrieved 2010-08-20.
Jump up ^ Heydenreich, Ludwig Heinrich (2017-04-30). "Leonardo da Vinci". Encyclopædia Britannica. Encyclopædia Britannica, inc. Retrieved 2017-06-24.
Jump up ^ "Ten Books on Architecture. Book III, Chapter I, "On Symmetry: In Temples And In The Human Body"". Gutenberg.org. 2006-12-31. Retrieved 2010-08-20.
Jump up ^ "The Vitruvian Man and the squaring of the circle detailed". Arthistory.about.com. 2010-06-15. Retrieved 2010-08-20.
Jump up ^ "Bibliographic reference". The Whole Universe Book. Retrieved 2011-11-30.
Jump up ^ Bossi, Giuseppe (1810)Del Cenacolo di Leonardo da Vinci libri quattro (in Italian) Milano: Stamperia Reale p.208ff
Jump up ^ "Bibliographic reference". Ursusbooks.com. Retrieved 2010-08-20.
Jump up ^ "Bibliographical notice, no. 319". Lib.rochester.edu. Archived from the original on 7 January 2009. Retrieved 20 August 2010.
Jump up ^ "LEONARDO DA VINCI. THE UNIVERSAL MAN". venezia.net. Retrieved 2014-02-13.
Jump up ^ "The Other Vitruvian Man". Smithsonian Magazine. 2012-02-01.
Jump up ^ "Did Leonardo da Vinci copy his famous 'Vitruvian Man'?". 2012-01-31.
Sources[edit]
Lester, Toby (2012). Da Vinci's Ghost: Genius, Obsession, and How Leonardo Created the World in His Own Image. New York: Free Press. ISBN 9781439189238.
External links[edit]
Look up Vitruvian Man in Wiktionary, the free dictionary.
Wikimedia Commons has media related to Vitruvian Man.
Willamette University site on Vitruvian Man
Stanford University site on Vitruvian Man
Leonardo's Vitruvian Man
Vitruvian Man Video
Contemporary interpretation of Vitruvian Man
Leonardo da Vinci: anatomical drawings from the Royal Library, Windsor Castle, exhibition catalog fully online as PDF from The Metropolitan Museum of Art, which contains material on Vitruvian Man (see index)
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text:
I Can Only Imagine (film)
From Wikipedia, the free encyclopedia
Jump to: navigation, search
I Can Only Imagine
Theatrical release poster
Directed by
Erwin Brothers
Produced by
Cindy Bond
Kevin Downes
Daryl Lefever
Mickey Liddell
Pete Shilaimon
Raymond Harris
Joe Knopp
Screenplay by
Jon Erwin
Brent McCorkle
Story by
Alex Cramer
Jon Erwin
Brent McCorkle
Based on
The life story of
Bart Millard
Starring
J. Michael Finley
Madeline Carroll
Trace Adkins
Priscilla Shirer
Cloris Leachman
Dennis Quaid
Music by
Brent McCorkle
Cinematography
Kristopher Kimlin
Edited by
Andrew Erwin
Brent McCorkle
Production
companies
Kevin Downes Productions
Mission Pictures International
Distributed by
Lionsgate
Roadside Attractions[1]
Release date
March 16, 2018 (2018-03-16)
Running time
110 minutes
Country
United States
Language
English
Budget
$7 million[2]
Box office
$80.4 million[2]
I Can Only Imagine is a 2018 American Christian drama film directed by the Erwin Brothers and written by Alex Cramer, Jon Erwin and Brent McCorkle, based on the story behind the MercyMe song of the same name, the best-selling Christian single of all time.[3] The film stars J. Michael Finley as Bart Millard, the lead singer who wrote the song about his relationship with his father (Dennis Quaid). Madeline Carroll, Priscilla Shirer, Cloris Leachman, and Trace Adkins also star.
I Can Only Imagine was released in the United States on March 16, 2018. It has grossed $80 million worldwide against a production budget of $7 million, and is the third highest-grossing music biopic of all-time in the United States. Some critics praised it as inspiring and noted it as an improvement compared to other faith-based films, while others called it flat and by-the-numbers.
Contents
[hide]
1 Plot
2 Cast
3 Production
4 Reception
4.1 Box office
4.2 Critical response
5 References
6 External links
Plot[edit]
Coming from a home with an abusive father, 10-year-old Bart Millard is dropped off at a Christian camp by his mother, where he meets Shannon. Upon his return from camp, Bart finds his mother has left and movers are removing her belongings, leading to a physical confrontation with his father Arthur, who takes custody of him.
Years later, Millard is in high school and dating Shannon. Hoping to impress his father, he begins playing football. However, he is injured, breaking both ankles and ending his career. In order to make up the credits he would miss from football, he signs up for music class, the only available class left. Initially, Millard is assigned to be a sound technician. After the director catches him singing in the empty auditorium, she casts him as Curly, the lead role in the school production of Oklahoma. He doesn't tell his father of his role in the play, and while Bart has risen to the singing demands of the part, Arthur subsequently collapses with severe abdominal pain, but refuses to tell Bart or Shannon about his cancer diagnosis. The following morning, Millard voices his frustrations with his father and is assaulted by his father, who smashes a plate over his head. Shannon presses Bart to open up, but he responds by breaking up with her and leaving to seek his fortune in the city after graduation.
Millard then connects with the band who would become MercyMe, and eventually attracts the attention of Christian music producer Scott Brickell, who coaches Millard and gets them a showcase in Nashville, leading to meetings with Amy Grant and Michael W. Smith. Millard has been unable to reconcile with Shannon, who rejects his invitation to tour with the band to their upcoming Nashville showcase. While the band felt their performance was the best of their career, industry representatives reject the band as not being good enough to sell records. In despair, Millard quits the band. After talking with Brickell, who encourages him to resolve his issues with his father, Millard rejoins the band but asks they wait for him to confront his father and settle the conflict before they play again.
Millard returns home and is greeted enthusiastically by his father, who prepares breakfast for him the next morning. His father claims to have become a Christian, but Bart is initially skeptical and rejects his father's offer to start over. In anger, Arthur takes a baseball bat and hits his old Jeep, which he had hoped to repair with his son. As Bart prepares to leave, he finds out his father has terminal cancer. Bart and his father reconcile and form a deep bond before Arthur dies of his illness.
After his father's funeral, Millard rejoins the band and writes "I Can Only Imagine". Brickell sends the demo tape to several artists, including Grant, who, deeply moved by the song, asks to record it herself as her next single. The band accepts Grant's offer. Millard is seated in the audience when Grant intends to unveil the song live in concert. However, Grant decides that Millard should be the one to sing the song, and offers it back to him, calling him to the microphone to sing it before the large audience. Millard's unexpected performance is wildly applauded and he reunites with Shannon, who had also been in attendance at the concert. The band releases the song as their first single, achieving success on Christian and mainstream radio.
Cast[edit]
This list is incomplete; you can help by expanding it.
J. Michael Finley as Bart Millard
Brody Rose as Young Bart
Dennis Quaid as Arthur Millard, Bart's father
Cloris Leachman as Meemaw, Bart's grandmother
Madeline Carroll as Shannon, Bart's girlfriend
Taegen Burns as Young Shannon
Trace Adkins as Scott Brickell, MercyMe's manager
Priscilla Shirer as Mrs. Fincher, Bart's teacher
Nicole DuPort as Amy Grant
Jake B. Miller as Michael W. Smith
Mark Furze as Nathan
Production[edit]
This section needs expansion. You can help by adding to it. (March 2018)
The film was announced in December 2016.[4] Dennis Quaid joined the cast in January 2017.[5] Broadway actor J. Michael Finley makes his film debut as Bart Millard.[1] The same month, it was announced that the film was slated for release in the spring of 2018.[5] In August 2017, Lionsgate and Roadside Attractions signed on as distributors for the film for a nationwide release in the United States.[1][6]
Reception[edit]
Box office[edit]
As of April 22, 2018[update], I Can Only Imagine has grossed $79.4 million in the United States, and Canada and $1 million in other territories, for a worldwide total of $80.4 million, against a production budget of $7 million.[2] It is the third-highest grossing music biopic of all-time in the United States, behind Straight Outta Compton and Walk the Line.[7]
The film was released on March 16, 2018, alongside Tomb Raider and Love, Simon, and was originally projected to gross $2–4 million from 1,620 theaters in its opening weekend.[8] However, after making $6.2 million on its first day (including $1.3 million from Thursday night previews), weekend estimates were increased to $14 million.[9] It ended up grossing $17.1 million, exceeding expectations and finishing third at the box office behind Black Panther and Tomb Raider. 67% of the opening weekend audience was female while 80% was over the age of 35.[10] It was the fourth best-ever opening for a faith-based film, following The Passion of the Christ ($83.8 million), Son of God ($25.6 million) and Heaven Is for Real ($22.5 million).[11] In its second weekend the film was added to 624 additional theaters and dropped just 19% to $13.8 million, again finishing third.[12] It was added to another 395 venues and finished fourth in its third weekend, making $10.4 million (including $3 million on Easter Sunday).[13]
Critical response[edit]
On review aggregator website Rotten Tomatoes, the film holds an approval rating of 70% based on 23 reviews, and an average rating of 6/10. The site's critical consensus reads, "I Can Only Imagine's message will have the most impact among Christian audiences, but overall, its performances and storytelling represent a notable evolution in faith-based cinema."[14] On Metacritic, the film has a weighted average score of 29 out of 100, based on 7 critics, indicating "generally unfavorable reviews".[15] Audiences polled by CinemaScore gave the film an average grade of "A+" on an A+ to F scale, one of fewer than 80 films in the history of the service to earn such a score.[9]
The Arizona Republic's James Ward gave the film 4/5 stars and wrote, "Too often faith-based films — say anything with Kirk Cameron or the terrible God's Not Dead series — tend to preach to the choir or hector their audience. The Erwins’ films — I Can Only Imagine definitely among them — are more inclusive, charitable of spirit and hopeful, all qualities that are always appreciated, be they rooted in Christian faith or otherwise."[16] David Ehrlich of IndieWire gave the film a "C–" saying: "There’s a reason why all of these movies are so amateurishly made; why they all end with links to religious websites; why they all look like they were shot on an iPhone by a Walmart-brand Janusz Kaminski who lit each interior like the white light of heaven was streaming through every window...Art can be affirmation, but affirmation cannot be art."[17]
References[edit]
^ Jump up to: a b c Anita, Busch (August 14, 2017). "Faith-Based 'I Can Only Imagine' Picked Up By Lionsgate & Roadside Attractions". Deadline Hollywood. Penske Business Media. Retrieved March 19, 2018.
^ Jump up to: a b c "I Can Only Imagine (2018)". The Numbers. Retrieved April 22, 2018.
Jump up ^ Ryan, Patrick (March 13, 2018). "How did 'I Can Only Imagine' become the biggest Christian hit ever (and inspire a movie)?". USA Today. Retrieved April 18, 2018.
Jump up ^ Staff Reports (December 2, 2016). "MercyMe hit song turning into Okie production". NewsOK. Retrieved March 19, 2018.
^ Jump up to: a b Busch, Anita (January 5, 2017). "Faith-Based Film 'I Can Only Imagine' With Dennis Quaid, Cloris Leachman And Trace Adkins Eyes Spring 2018 Release". Deadline Hollywood. Penske Business Media. Retrieved March 19, 2018.
Jump up ^ Galuppo, Mia (August 14, 2017). "Lionsgate, Roadside Acquire Faith-Based Drama 'I Can Only Imagine'". The Hollywood Reporter. Retrieved March 19, 2018.
Jump up ^ "Biopic - Music Movies at the Box Office". Box Office Mojo. Retrieved April 20, 2018.
Jump up ^ Fuster, Jeremy (March 13, 2018). "Will 'Tomb Raider' Be the Movie to Finally Knock 'Black Panther' From Box Office Perch?". TheWrap. Retrieved March 19, 2018.
^ Jump up to: a b D'Alessandro, Anthony (March 16, 2018). "'Black Panther' Poised For $460M+ In Profit; 'I Can Only Imagine' Surprises – Box Office Update". Deadline Hollywood. Penske Business Media. Retrieved March 17, 2018.
Jump up ^ D'Alessandro, Anthony (March 18, 2018). "'Black Panther' Keeps B.O. Treasure From 'Tomb Raider'; How 'I Can Only Imagine' Hit A $17M High Note". Deadline Hollywood. Penske Business Media. Retrieved March 18, 2018.
Jump up ^ "Box Office: 'I Can Only Imagine' Revives Faith-Based Genre". The Hollywood Reporter. March 20, 2018. Retrieved March 20, 2018.
Jump up ^ D'Alessandro, Anthony (March 25, 2018). "Does 'Pacific Rim: Uprising' Break Even At The Global B.O.?; 'Black Panther' Sets Marvel Record – Sunday Postmortem". Deadline Hollywood. Retrieved March 25, 2018.
Jump up ^ D'Alessandro, Anthony (April 1, 2018). "How Warner Bros. Sold 'Ready Player One' On The Spielberg Spirit & Beat Tracking With $53M+ 4-Day – Sunday Postmortem". Deadline Hollywood. Retrieved April 4, 2018.
Jump up ^ "I Can Only Imagine (2018)". Rotten Tomatoes. Fandango. Retrieved March 29, 2018.
Jump up ^ "I Can Only Imagine Reviews". Metacritic. CBS Interactive. Retrieved March 22, 2018.
Jump up ^ Ward, James (March 15, 2018). "Faith-based drama 'I Can Only Imagine' doesn't just preach to the choir". The Arizona Republic. Retrieved March 22, 2018.
Jump up ^ Ehrlich, David (March 21, 2018). "'I Can Only Imagine' Review: A Christian Rock Biopic Shows Why Faith-Based Films Struggle to Convert Secular Audiences". IndieWire. Retrieved March 22, 2018.
External links[edit]
Official website
I Can Only Imagine on IMDb
I Can Only Imagine at AllMovie
I Can Only Imagine at Box Office Mojo
Retrieved from "https://en.wikipedia.org/w/index.php?title=I_Can_Only_Imagine_(film)&oldid=837811469"
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Natural-language processing
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about language processing by computers. For the processing of language by the human brain, see Language processing in the brain.
An automated online assistant providing customer service on a web page, an example of an application where natural-language processing is a major component.[1]
Natural-language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data.
Challenges in natural-language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.
Contents
[hide]
1 History
2 Statistical natural-language processing (SNLP)
3 Major evaluations and tasks
3.1 Syntax
3.2 Semantics
3.3 Discourse
3.4 Speech
4 See also
5 References
6 Further reading
7 External links
History[edit]
The history of natural-language processing generally started in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.
The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem.[2] However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted until the late 1980s, when the first statistical machine translation systems were developed.
Some notably successful natural-language processing systems developed in the 1960s were SHRDLU, a natural-language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966. Using almost no information about human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction. When the "patient" exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to "My head hurts" with "Why do you say your head hurts?".
During the 1970s, many programmers began to write "conceptual ontologies", which structured real-world information into computer-understandable data. Examples are MARGIE (Schank, 1975), SAM (Cullingford, 1978), PAM (Wilensky, 1978), TaleSpin (Meehan, 1976), QUALM (Lehnert, 1977), Politics (Carbonell, 1979), and Plot Units (Lehnert 1981). During this time, many chatterbots were written including PARRY, Racter, and Jabberwacky.
Up to the 1980s, most natural-language processing systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in natural-language processing with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power (see Moore's law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing.[3] Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. However, part-of-speech tagging introduced the use of hidden Markov models to natural-language processing, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks.
Many of the notable early successes occurred in the field of machine translation, due especially to work at IBM Research, where successively more complicated statistical models were developed. These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. However, most other systems depended on corpora specifically developed for the tasks implemented by these systems, which was (and often continues to be) a major limitation in the success of these systems. As a result, a great deal of research has gone into methods of more effectively learning from limited amounts of data.
Recent research has increasingly focused on unsupervised and semi-supervised learning algorithms. Such algorithms are able to learn from data that has not been hand-annotated with the desired answers, or using a combination of annotated and non-annotated data. Generally, this task is much more difficult than supervised learning, and typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the World Wide Web), which can often make up for the inferior results.
In recent years, there has been a flurry of results showing deep learning techniques[4][5] achieving state-of-the-art results in many natural-language tasks, for example in language modeling,[6] parsing,[7][8] and many others.
Statistical natural-language processing (SNLP)[edit]
Since the so-called "statistical revolution"[9][10] in the late 1980s and mid 1990s, much natural-language processing research has relied heavily on machine learning.
Formerly, many language-processing tasks typically involved the direct hand coding of rules,[11][12] which is not in general robust to natural-language variation. The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large corpora of typical real-world examples (a corpus (plural, "corpora") is a set of documents, possibly with human or computer annotations).
Many different classes of machine learning algorithms have been applied to natural-language processing tasks. These algorithms take as input a large set of "features" that are generated from the input data. Some of the earliest-used algorithms, such as decision trees, produced systems of hard if-then rules similar to the systems of hand-written rules that were then common. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to each input feature. Such models have the advantage that they can express the relative certainty of many different possible answers rather than only one, producing more reliable results when such a model is included as a component of a larger system.
Systems based on machine-learning algorithms have many advantages over hand-produced rules:
The learning procedures used during machine learning automatically focus on the most common cases, whereas when writing rules by hand it is often not at all obvious where the effort should be directed.
Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to unfamiliar input (e.g. containing words or structures that have not been seen before) and to erroneous input (e.g. with misspelled words or words accidentally omitted). Generally, handling such input gracefully with hand-written rules—or more generally, creating systems of hand-written rules that make soft decisions—is extremely difficult, error-prone and time-consuming.
Systems based on automatically learning the rules can be made more accurate simply by supplying more input data. However, systems based on hand-written rules can only be made more accurate by increasing the complexity of the rules, which is a much more difficult task. In particular, there is a limit to the complexity of systems based on hand-crafted rules, beyond which the systems become more and more unmanageable. However, creating more data to input to machine-learning systems simply requires a corresponding increase in the number of man-hours worked, generally without significant increases in the complexity of the annotation process.
Major evaluations and tasks[edit]
The following is a list of some of the most commonly researched tasks in natural-language processing. Note that some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks.
Though natural-language processing tasks are closely intertwined, they are frequently subdivided into categories for convenience. A coarse division is given below.
Syntax[edit]
Lemmatization
Morphological segmentation
Separate words into individual morphemes and identify the class of the morphemes. The difficulty of this task depends greatly on the complexity of the morphology (i.e. the structure of words) of the language being considered. English has fairly simple morphology, especially inflectional morphology, and thus it is often possible to ignore this task entirely and simply model all possible forms of a word (e.g. "open, opens, opened, opening") as separate words. In languages such as Turkish or Meitei,[13] a highly agglutinated Indian language, however, such an approach is not possible, as each dictionary entry has thousands of possible word forms.
Part-of-speech tagging
Given a sentence, determine the part of speech for each word. Many words, especially common ones, can serve as multiple parts of speech. For example, "book" can be a noun ("the book on the table") or verb ("to book a flight"); "set" can be a noun, verb or adjective; and "out" can be any of at least five different parts of speech. Some languages have more such ambiguity than others. Languages with little inflectional morphology, such as English, are particularly prone to such ambiguity. Chinese is prone to such ambiguity because it is a tonal language during verbalization. Such inflection is not readily conveyed via the entities employed within the orthography to convey intended meaning.
Parsing
(see also: Stochastic grammar) Determine the parse tree (grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses. In fact, perhaps surprisingly, for a typical sentence there may be thousands of potential parses (most of which will seem completely nonsensical to a human). There are two primary types of parsing, Dependency Parsing and Constituency Parsing. Dependency Parsing focuses on the relationships between words in a sentence (marking things like Primary Objects and predicates), whereas Constituency Parsing focuses on building out the Parse Tree using a Probabilistic Context-Free Grammar (PCFG).
Sentence breaking (also known as sentence boundary disambiguation)
Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by periods or other punctuation marks, but these same characters can serve other purposes (e.g. marking abbreviations).
Stemming
Word segmentation
Separate a chunk of continuous text into separate words. For a language like English, this is fairly trivial, since words are usually separated by spaces. However, some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language.
Terminology extraction
The goal of terminology extraction is to automatically extract relevant terms from a given corpus.
Semantics[edit]
Lexical semantics
What is the computational meaning of individual words in context?
Machine translation
Automatically translate text from one human language to another. This is one of the most difficult problems, and is a member of a class of problems colloquially termed "AI-complete", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) in order to solve properly.
Named entity recognition (NER)
Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is (e.g. person, location, organization). Note that, although capitalization can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of named entity, and in any case is often inaccurate or insufficient. For example, the first word of a sentence is also capitalized, and named entities often span several words, only some of which are capitalized. Furthermore, many other languages in non-Western scripts (e.g. Chinese or Arabic) do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names. For example, German capitalizes all nouns, regardless of whether they refer to names, and French and Spanish do not capitalize names that serve as adjectives.
Natural language generation
Convert information from computer databases or semantic intents into readable human language.
Natural language understanding
Convert chunks of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate. Natural-language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural-language expression which usually takes the form of organized notations of natural-language concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural-language semantics without confusions with implicit assumptions such as closed-world assumption (CWA) vs. open-world assumption, or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.[14]
Optical character recognition (OCR)
Given an image representing printed text, determine the corresponding text.
Question answering
Given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?"). Recent works have looked at even more complex questions.[15]
Recognizing Textual entailment
Given two text fragments, determine if one being true entails the other, entails the other's negation, or allows the other to be either true or false.[16]
Relationship extraction
Given a chunk of text, identify the relationships among named entities (e.g. who is married to whom).
Sentiment analysis
Extract subjective information usually from a set of documents, often using online reviews to determine "polarity" about specific objects. It is especially useful for identifying trends of public opinion in the social media, for the purpose of marketing.
Topic segmentation and recognition
Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment.
Word sense disambiguation
Many words have more than one meaning; we have to select the meaning which makes the most sense in context. For this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or from an online resource such as WordNet.
Discourse[edit]
Automatic summarization
Produce a readable summary of a chunk of text. Often used to provide summaries of text of a known type, such as articles in the financial section of a newspaper.
Coreference resolution
Given a sentence or larger chunk of text, determine which words ("mentions") refer to the same objects ("entities"). Anaphora resolution is a specific example of this task, and is specifically concerned with matching up pronouns with the nouns or names to which they refer. The more general task of coreference resolution also includes identifying so-called "bridging relationships" involving referring expressions. For example, in a sentence such as "He entered John's house through the front door", "the front door" is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John's house (rather than of some other structure that might also be referred to).
Discourse analysis
This rubric includes a number of related tasks. One task is identifying the discourse structure of connected text, i.e. the nature of the discourse relationships between sentences (e.g. elaboration, explanation, contrast). Another possible task is recognizing and classifying the speech acts in a chunk of text (e.g. yes-no question, content question, statement, assertion, etc.).
Speech[edit]
Speech recognition
Given a sound clip of a person or people speaking, determine the textual representation of the speech. This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed "AI-complete" (see above). In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary subtask of speech recognition (see below). Note also that in most spoken languages, the sounds representing successive letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to discrete characters can be a very difficult process.
Speech segmentation
Given a sound clip of a person or people speaking, separate it into words. A subtask of speech recognition and typically grouped with it.
Text-to-speech
See also[edit]
Automated essay scoring
Biomedical text mining
Compound term processing
Computational linguistics
Computer-assisted reviewing
Controlled natural language
Deep learning
Deep linguistic processing
Foreign language reading aid
Foreign language writing aid
Information extraction
Information retrieval
Language technology
Latent Dirichlet allocation (LDA)
Latent semantic indexing
List of natural language processing toolkits
Native-language identification
Natural language programming
Natural language search
Query expansion
Reification (linguistics)
Semantic folding
Speech processing
Spoken dialogue system
Text-proofing
Text simplification
Thought vector
Truecasing
Question answering
Word2vec
Hierarchical Deep Learning
References[edit]
Jump up ^ Implementing an online help desk system based on conversational agent Authors: Alisa Kongthon, Chatchawal Sangkeettrakarn, Sarawoot Kongyoung and Choochart Haruechaiyasak. Published by ACM 2009 Article, Bibliometrics Data Bibliometrics. Published in: Proceeding, MEDES '09 Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM New York, NY, USA. ISBN 978-1-60558-829-2, doi:10.1145/1643823.1643908
Jump up ^ Hutchins, J. (2005). "The history of machine translation in a nutshell". [self-published source]
Jump up ^ Chomskyan linguistics encourages the investigation of "corner cases" that stress the limits of its theoretical models (comparable to pathological phenomena in mathematics), typically created using thought experiments, rather than the systematic investigation of typical phenomena that occur in real-world data, as is the case in corpus linguistics. The creation and use of such corpora of real-world data is a fundamental part of machine-learning algorithms for natural-language processing. In addition, theoretical underpinnings of Chomskyan linguistics such as the so-called "poverty of the stimulus" argument entail that general learning algorithms, as are typically used in machine learning, cannot be successful in language processing. As a result, the Chomskyan paradigm discouraged the application of such models to language processing.
Jump up ^ Goldberg, Yoav (2016). A Primer on Neural Network Models for Natural Language Processing. Journal of Artificial Intelligence Research 57 (2016) 345–420
Jump up ^ Ian Goodfellow, Yoshua Bengio and Aaron Courville. http://www.deeplearningbook.org/ Deep Learning]. MIT Press.
Jump up ^ Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu (2016). https://arxiv.org/abs/1602.02410 Exploring the Limits of Language Modeling
Jump up ^ Do Kook Choe and Eugene Charniak (EMNLP 2016). http://www.aclweb.org/website/old_anthology/D/D16/D16-1257.pdf Parsing as Language Modeling
Jump up ^ Vinyals, Oriol, et al. (NIPS2015). https://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf
Jump up ^ Mark Johnson. How the statistical revolution changes (computational) linguistics. Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics.
Jump up ^ Philip Resnik. Four revolutions. Language Log, February 5, 2011.
Jump up ^ Winograd, Terry (1971). Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. http://hci.stanford.edu/winograd/shrdlu/
Jump up ^ Roger C. Schank and Robert P. Abelson (1977). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures
Jump up ^ Kishorjit, N., Vidya Raj RK., Nirmal Y., and Sivaji B. (2012) "Manipuri Morpheme Identification", Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing (SANLP), pages 95–108, COLING 2012, Mumbai, December 2012
Jump up ^ Yucong Duan, Christophe Cruz (2011), Formalizing Semantic of Natural Language through Conceptualization from Existence.[dead link] International Journal of Innovation, Management and Technology(2011) 2 (1), pp. 37-42.
Jump up ^ "Versatile question answering systems: seeing in synthesis", Mittal et al., IJIIDS, 5(2), 119-142, 2011.
Jump up ^ PASCAL Recognizing Textual Entailment Challenge (RTE-7) https://tac.nist.gov//2011/RTE/
Further reading[edit]
Bates, M (1995). "Models of natural language understanding". Proceedings of the National Academy of Sciences of the United States of America. 92 (22): 9977–9982. doi:10.1073/pnas.92.22.9977. PMC 40721  .
Steven Bird, Ewan Klein, and Edward Loper (2009). Natural Language Processing with Python. O'Reilly Media. ISBN 978-0-596-51649-9.
Daniel Jurafsky and James H. Martin (2008). Speech and Language Processing, 2nd edition. Pearson Prentice Hall. ISBN 978-0-13-187321-6.
Mohamed Zakaria Kurdi (2016). Natural Language Processing and Computational Linguistics: speech, morphology, and syntax, Volume 1. ISTE-Wiley. ISBN 978-1848218482.
Mohamed Zakaria Kurdi (2017). Natural Language Processing and Computational Linguistics: semantics, discourse, and applications, Volume 2. ISTE-Wiley. ISBN 978-1848219212.
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze (2008). Introduction to Information Retrieval. Cambridge University Press. ISBN 978-0-521-86571-5. Official html and pdf versions available without charge.
Christopher D. Manning and Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. The MIT Press. ISBN 978-0-262-13360-9.
David M. W. Powers and Christopher C. R. Turk (1989). Machine Learning of Natural Language. Springer-Verlag. ISBN 978-0-387-19557-5.
External links[edit]
Media related to Natural-language processing at Wikimedia Commons
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v
t
e
Natural language processing
General terms
Text corpus
Speech corpus
Stopwords
Bag-of-words
AI-complete
n-gram (Bigram, Trigram)
Text analysis
Text segmentation
Part-of-speech tagging
Text chunking
Compound term processing
Collocation extraction
Stemming
Lemmatisation
Named-entity recognition
Coreference resolution
Sentiment analysis
Concept mining
Parsing
Word-sense disambiguation
Terminology extraction
Truecasing
Automatic summarization
Multi-document summarization
Sentence extraction
Text simplification
Machine translation
Computer-assisted
Example-based
Rule-based
Automatic identification
and data capture
Speech recognition
Speech synthesis
Optical character recognition
Natural language generation
Topic model
Pachinko allocation
Latent Dirichlet allocation
Latent semantic analysis
Computer-assisted
reviewing
Automated essay scoring
Concordancer
Grammar checker
Predictive text
Spell checker
Syntax guessing
Natural language
user interface
Automated online assistant
Chatbot
Interactive fiction
Question answering
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NDL: 00562347
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